Intelligent recognition and alert methods and systems

ABSTRACT

An intelligent target object detection and alerting platform may be provided. The platform may receive a content stream from a content source. A target object may be designated for detection within the content stream. A target object profile associated with the designated target object may be retrieved from a database of learned target object profiles. The learned target object profiles may be associated with target objects that have been trained for detection. At least one frame associated with the content stream may be analyzed to detect the designated target object. The analysis may comprise employing a neural net, for example, to detect each target object within each frame. A parameter for communicating target object detection data may be specified. In turn, when the parameter is met, the detection data may be communicated.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/001,336 filed on Aug. 24, 2020, which issues Feb. 15, 2022 as U.S.Pat. No. 11,250,324, which is a continuation of U.S. application Ser.No. 16/297,502 filed on Mar. 8, 2019, which issued on Sep. 15, 2020 asU.S. Pat. No. 10,776,695, which are hereby incorporated by referenceherein in its entirety.

It is intended that the above-referenced application may be applicableto the concepts and embodiments disclosed herein, even if such conceptsand embodiments are disclosed in the referenced applications withdifferent limitations and configurations and described using differentexamples and terminology.

FIELD OF DISCLOSURE

The present disclosure generally relates to intelligent filtering andintelligent alerts for target object detection in a content source.

BACKGROUND

Trail cameras and surveillance cameras often send image data that may beinterpreted as false positives for detection of certain objects. Thesefalse positives can be caused by the motion of inanimate objects likelimbs or leaves. False positives can also be caused by the movement ofanimate objects that are not being studied or pursued. The conventionalstrategy is to provide an end user with all captured footage. This oftencauses problems because the conventional strategy requires the end userto scour through a plurality of potentially irrelevant frames.

Furthermore, to provide just one example of a technical problem that maybe addressed by the present disclosure, it is becoming increasinglyimportant to monitor cervid populations and track the spread chronicdiseases, including, without limitation, Chronic Wasting Disease (CWD).CWD has been found in approximately 50% of the states within the UnitedStates, and attempts must be made to contain the spread and eradicateaffected animals. This often causes problems because the conventionalstrategy does not address the recognition of affected populations earlyenough to prevent further spreading of the disease.

Finally, it is also becoming increasingly important to monitor themakeup of animal populations based on age, sex and species. Being ableto monitor by such categories allows interested parties, such as theDepartment of Natural Resources in various states, to properly track andmonitor the overall health of large populations of relevant specieswithin the respective state.

BRIEF OVERVIEW

This brief overview is provided to introduce a selection of concepts ina simplified form that are further described below in the DetailedDescription. This brief overview is not intended to identify keyfeatures or essential features of the claimed subject matter. Nor isthis brief overview intended to be used to limit the claimed subjectmatter's scope.

Embodiments of the present disclosure may provide a method forintelligent recognition and alerting. The method may begin withreceiving a content stream from a content source, the content sourcecomprising at least one of the following: a capturing device, and auniform resource locator. At least one target object may be designatedfor detection within the content stream. A target object profileassociated with each designated target object may be retrieved from adatabase of learned target object profiles. The database of learnedtarget object profiles may be associated with target objects that havebeen trained for detection. Accordingly, at least one frame associatedwith the content stream may be analyzed for each designated targetobject. The analysis may comprise employing a neural net, for example,to detect each target object within each frame by matching aspects ofeach object within a frame to aspects of the at least one learned targetobject profile.

At least one parameter for communicating target object detection datamay be specified to notify an interested party of detection data. The atleast one parameter may comprise, but not be limited to, for example: atleast one aspect of the at least one detected target object and at leastone aspect of the content source. In turn, when the at least oneparameter is met, the target object detection data may be communicated.The communication may comprise, for example, but not be limited to,transmitting the at least one frame along with annotations associatedwith the detected at least one target object and transmitting anotification comprising the target object detection data.

Still consistent with embodiments of the present disclosure, an AIEngine may be provided. The AI engine may comprise, but not be limitedto, for example, a content module, a recognition module, and an analysismodule.

The content module may be configured to receive a content stream from atleast one content source.

The recognition module may be configured to:

-   -   match aspects of the content stream to at least one learned        target object profile from a database of learned target object        profiles to detect target objects within the content, and upon a        determination that at least one of the detected target objects        corresponds to the at least one learned target object profile:        -   classify the at least one detected target object based on            the at least one learned target object profile, and        -   update the at least one learned target object profile with            at least one aspect of the at least one detected target            object.

The analysis module may be configured to:

-   -   process the at least one detected target object through a neural        net for a detection of learned features associated with the at        least one detected target object, wherein the learned features        are specified by the at least one learned target object profile        associated with the at least one detected target object,    -   determine, based on the process, the following:        -   a species of the at least one detected target object,        -   a sub-species of the at least one detected target object,        -   a gender of the at least one detected target object,        -   an age of the at least one detected target object,        -   a health of the at least one detected target object, and        -   a score for the at least one detected target object, and        -   update the learned target object profile with the detected            learned features.

In yet further embodiments of the present disclosure, a systemcomprising at least one capturing device, at least one end-user device,and an AI engine may be provided.

The least one capturing device may be configured to:

-   -   register with an AI engine,    -   capture at least one of the following:        -   visual data, and        -   audio data,        -   digitize the captured data, and        -   transmit the digitized data as at least one content stream            to the AI engine.

The at least one end-user device may be configured to:

-   -   configure the at least one capturing device to be in operative        communication with the AI engine,    -   define at least one zone, wherein the at least one end-user        device being configured to define the at least one zone        comprises the at least one end-user device being configured to:        -   specify at least one content source for association with the            at least one zone, and        -   specify the at least one content stream associated with the            at least one content source, the specified at least one            content stream to be processed by the AI engine for the at            least one zone,    -   specify at least one zone parameter from a plurality of zone        parameters for the at least one zone, wherein the zone        parameters comprise:        -   a plurality of selectable target object designations for            detection within the at least one zone, the target object            designations being associated with a plurality of learned            target object profiles trained by the AI engine,    -   specify at least one alert parameter from a plurality of alert        parameters for the at least one zone, wherein the alert        parameters comprise:        -   triggers for an issuance of an alert,        -   recipients that receive the alert,        -   actions to be performed when an alert is triggered, and        -   restrictions on issuing the alert,    -   receive the alert from the AI engine, and    -   display the detected target object related data associated with        the alert, wherein the detected target object related data        comprises at least one frame from the at least one content        stream.

The AI engine of the system may comprise a content module, a recognitionmodule, an analysis module, and an interface layer.

The content module may be configured to receive the content stream fromthe at least one capturing device.

The recognition module may be configured to:

-   -   match aspects of the content stream to at least one learned        target object profile in a database of the plurality of learned        target object profiles trained by the AI engine to detect target        objects within the content, and upon a determination that at        least one of the detected target objects corresponds to the at        least one learned target object profile:        -   classify the at least one detected target object based on            the at least one learned target object profile, and        -   update the at least one learned target object profile with            at least one aspect of the at least one detected target            object;

an analysis module configured to:

-   -   process the at least one detected target object through a neural        net for a detection of learned features associated with the at        least one detected target object, wherein the learned features        are specified by the at least one learned target object profile        associated with the at least one detected target object,    -   determine, based on the process, the following attributes of the        at least one detected target object:        -   a species of the at least one detected target object,        -   a sub-species of the at least one detected target object,        -   a gender of the at least one detected target object,        -   an age of the at least one detected target object,        -   a health of the at least one detected target object, and        -   a score for the at least one detected target object,    -   update the learned target object profile with the detected        learned features, and    -   determine whether the at least one detected target object        corresponds to at least one of the target object designations        associated with the zone specified at the end-user device, and    -   determine whether the attributes associated with the at least        one detected object correspond to the triggers for the issuance        of the alert.

The interface layer may be configured to:

-   -   communicate the detected target object data to the at least one        end-user device, wherein the detected target object related data        comprises at least one of the following:        -   at least one frame along with annotations associated with            the detected at least one target object, and        -   a push notification to the at least one end-user device.

Still consistent with embodiments of the present disclosure, a methodmay be provided. The method may comprise:

-   -   establishing at least one target object to detect within a        content stream, wherein establishing the at least one target        object to detect comprises:        -   identifying at least one target object profile from a            database of target object profiles;    -   establishing at least one parameter for assessing the at least        one target object, wherein establishing the at least one        parameter comprises:        -   specifying at least one of the following:            -   a species of the at least one detected target object,            -   a sub-species of the at least one detected target                object,            -   a gender of the at least one target object,            -   an age of the at least one target object,            -   a health of the at least one target object, and            -   a score for the at least one target object;    -   analyzing the at least one frame associated with the content        stream for the at least one target object;    -   detecting the at least one target object within the at least one        frame by matching aspects of the at least one frame to aspects        of the at least one target object profile; and    -   communicating target object detection data, wherein        communicating the target object detection data comprises at        least one of the following:        -   transmitting the at least one frame along with annotations            associated with the detected at least one target object,            wherein the annotations correspond to the at least one            parameter.

Still consistent with embodiments of the present disclosure, a systemmay be provided. The method may comprise:

-   -   at least one end-user device module configured to:        -   select from a plurality of content sources for providing a            content stream associated with each of the plurality of            content sources,        -   specify at least one zone for each selected content source,        -   specify at least one content source for association with the            at least one zone, and        -   specify a first zone detection parameter, wherein the first            zone parameter is specifying at least one target object from            a plurality of selectable target object designations for            detection within the at least one zone, the target object            designations being associated with a plurality of learned            target object profiles trained by the AI engine; and    -   an analysis module configured to:        -   process at least one frame of the content stream for a            detection of learned features associated with the at least            one target object, wherein the learned features are            specified by at least one learned target object profile            associated with the at least one target object,        -   detect the at least one target object within at least one            frame of by matching aspects of the at least one frame to            aspects of the at least one target object profile, and        -   determine, based on the processing, at least one of the            following attributes of the at least one detected target            object:            -   a species of the at least one detected target object,            -   a sub-species of the at least one detected target                object,            -   a gender of the at least one detected target object,            -   an age of the at least one detected target object,            -   a health of the at least one detected target object, and            -   a score for the at least one detected target object.

Both the foregoing brief overview and the following detailed descriptionprovide examples and are explanatory only. Accordingly, the foregoingbrief overview and the following detailed description should not beconsidered to be restrictive. Further, features or variations may beprovided in addition to those set forth herein. For example, embodimentsmay be directed to various feature combinations and sub-combinationsdescribed in the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various embodiments of the presentdisclosure. The drawings contain representations of various trademarksand copyrights owned by the Applicant. In addition, the drawings maycontain other marks owned by third parties and are being used forillustrative purposes only. All rights to various trademarks andcopyrights represented herein, except those belonging to theirrespective owners, are vested in and the property of the Applicant. TheApplicant retains and reserves all rights in its trademarks andcopyrights included herein, and grants permission to reproduce thematerial only in connection with reproduction of the granted patent andfor no other purpose.

Furthermore, the drawings may contain text or captions that may explaincertain embodiments of the present disclosure. This text is included forillustrative, non-limiting, explanatory purposes of certain embodimentsdetailed in the present disclosure. In the drawings:

FIG. 1 illustrates a block diagram of an operating environmentconsistent with some embodiments of the present disclosure;

FIG. 2 illustrates a block diagram of an operating environmentconsistent with some embodiments the present disclosure;

FIG. 3 illustrates a block diagram of an AI Engine consistent with someembodiments the present disclosure;

FIG. 4 is a flow chart of a method for AI training consistent with someembodiments the present disclosure;

FIG. 5 is a flow chart of another method for AI training consistent withsome embodiments the present disclosure;

FIG. 6 is a flow chart of a method for associating a content source witha zone consistent with some embodiments the present disclosure;

FIG. 7 is a flow chart of a method for defining parameters with a zoneconsistent with some embodiments the present disclosure;

FIG. 8 is a flow chart of a method for performing object recognitionconsistent with some embodiments the present disclosure;

FIG. 9 is a flow chart of another method for performing objectrecognition consistent with some embodiments the present disclosure;

FIG. 10 is a flow chart of a method for updating training dataconsistent with some embodiments the present disclosure;

FIG. 11 illustrates a block diagram of a zone consistent with someembodiments the present disclosure;

FIG. 12 illustrates a block diagram of a plurality of zones consistentwith some embodiments the present disclosure;

FIG. 13 illustrates screen captures of a user interface consistent withsome embodiments the present disclosure;

FIG. 14 illustrates screen captures of another user interface consistentwith some embodiments the present disclosure;

FIG. 15 illustrates screen captures of yet another user interfaceconsistent with some embodiments the present disclosure;

FIG. 16 illustrates screen captures of yet another user interfaceconsistent with some embodiments the present disclosure;

FIG. 17 illustrates image data consistent with some embodiments thepresent disclosure;

FIG. 18 illustrates additional image data consistent with someembodiments the present disclosure;

FIG. 19 illustrates more image data consistent with some embodiments thepresent disclosure;

FIG. 020 illustrates yet more image data consistent with someembodiments the present disclosure;

FIG. 21 illustrates even more image data consistent with someembodiments the present disclosure; and

FIG. 22 is a block diagram of a system including a computing device forperforming the various methods disclosed herein.

DETAILED DESCRIPTION

As a preliminary matter, it will readily be understood by one havingordinary skill in the relevant art that the present disclosure has broadutility and application. As should be understood, any embodiment mayincorporate only one or a plurality of the following aspects of thedisclosure and may further incorporate only one or a plurality of thefollowing features. Furthermore, any embodiment discussed and identifiedas being “preferred” is considered to be part of a best modecontemplated for carrying out the embodiments of the present disclosure.Other embodiments also may be discussed for additional illustrativepurposes in providing a full and enabling disclosure. Moreover, manyembodiments, such as adaptations, variations, modifications, andequivalent arrangements, will be implicitly disclosed by the embodimentsdescribed herein and fall within the scope of the present disclosure.

Accordingly, while embodiments are described herein in detail inrelation to one or more embodiments, it is to be understood that thisdisclosure is illustrative and exemplary of the present disclosure. Thedetailed disclosure herein of one or more embodiments is not intended,nor is to be construed, to limit the scope of patent protection affordedin any claim of a patent issuing here from, which scope is to be definedby the claims and the equivalents thereof. It is not intended that thescope of patent protection be defined by reading into any claim alimitation found herein that does not explicitly appear in the claimitself.

Thus, for example, any sequence(s) and/or temporal order of steps ofvarious processes or methods that are described herein are illustrativeand not restrictive. Accordingly, it should be understood that, althoughsteps of various processes or methods may be shown and described asbeing in a sequence or temporal order, the steps of any such processesor methods are not limited to being carried out in any particularsequence or order, absent an indication otherwise. Indeed, the steps insuch processes or methods generally may be carried out in variousdifferent sequences and orders while still falling within the scope ofthe present invention. Accordingly, it is intended that the scope ofpatent protection is to be defined by the issued claim(s) rather thanthe description set forth herein.

Additionally, it is important to note that each term used herein refersto that which an ordinary artisan would understand such term to meanbased on the contextual use of such term herein. To the extent that themeaning of a term used herein—as understood by the ordinary artisanbased on the contextual use of such term—differs in any way from anyparticular dictionary definition of such term, it is intended that themeaning of the term as understood by the ordinary artisan shouldprevail.

Regarding applicability of 35 U.S.C. § 112, ¶6, no claim element isintended to be read in accordance with this statutory provision unlessthe explicit phrase “means for” or “step for” is actually used in suchclaim element, whereupon this statutory provision is intended to applyin the interpretation of such claim element.

Furthermore, it is important to note that, as used herein, “a” and “an”each generally denotes “at least one,” but does not exclude a pluralityunless the contextual use dictates otherwise. When used herein to join alist of items, “or” denotes “at least one of the items,” but does notexclude a plurality of items of the list. Finally, when used herein tojoin a list of items, “and” denotes “all of the items of the list.”

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar elements.While many embodiments of the disclosure may be described,modifications, adaptations, and other implementations are possible. Forexample, substitutions, additions, or modifications may be made to theelements illustrated in the drawings, and the methods described hereinmay be modified by substituting, reordering, or adding stages to thedisclosed methods. Accordingly, the following detailed description doesnot limit the disclosure. Instead, the proper scope of the disclosure isdefined by the appended claims. The present disclosure contains headers.It should be understood that these headers are used as references andare not to be construed as limiting upon the subjected matter disclosedunder the header.

The present disclosure includes many aspects and features. Moreover,while many aspects and features relate to, and are described in, thecontext of animal detection and tracking, embodiments of the presentdisclosure are not limited to use only in this context. Rather, anycontext in which objects may be identified within a data stream inaccordance to the various methods and systems described herein may beconsidered within the scope and spirit of the present disclosure.

I. Platform Overview

This overview is provided to introduce a selection of concepts in asimplified form that are further described below. This overview is notintended to identify key features or essential features of the claimedsubject matter. Nor is this overview intended to be used to limit theclaimed subject matter's scope.

Embodiments of the present disclosure provide methods, systems, anddevices (collectively referred to herein as “the platform”) forintelligent object detection and alert filtering. The platform maycomprise an AI engine. The AI engine may be configured to processcontent (e.g., a video stream) received from one or more content sources(e.g., a camera). For example, the AI engine may be configured toconnect to remote cameras, online feeds, social networks, contentpublishing websites, and other user content designations. A user mayspecify one or more content sources for designation as a monitored zone.

Each monitored zone may be associated with target objects to detect andoptionally track within the content provided by the content source.Target objects may include, for example, but not be limited to: deer(buck, doe, diseased), pigs, fish, turkey, bobcat, human, and otheranimals. Target objects may also include inanimate objects, such as, butnot limited to vehicles (ATV, mail truck, etc.), drones, planes, anddevices. However, the scope of the present disclosure, as will bedetailed below, is not limited to any particular animate or inanimateobject. Furthermore, each zone may comprise alert parameters definingone or more actions to be performed by the platform upon a detection ofa target object.

In turn, the AI engine may monitor for the indication of target objectswithin the content associated with the zone. Accordingly, the contentmay be processed by the AI engine to detect target objects. Detection ofthe target objects may trigger alerts or notifications to one or moreinterested parties via a plurality of mediums. In this way, interestedparties may be provided with real-time information as to where and whenthe specified target objects are detected within the content sourcesand/or zones.

Further still, embodiments of the present disclosure may provide forintelligent filtering. Intelligent filtering may allow platform users toonly see content that contain target objects, thereby preventing contentoverload and ease of use. In this way, users will not need to scanthrough endless pictures of falling leaves, snowflakes, squirrels, thatwould otherwise trigger false detections.

Furthermore, the platform may provide activity reports, statistics, andother analytics that enable a user to track selected target objects anddetermine where and when, based on zone designation, those animals areactive. As will be detailed below, some implementations of the platformmay facilitate the detection, tracking, and assessment of diseasedanimals.

Embodiments of the present disclosure may comprise methods, systems, anda computer readable medium comprising, but not limited to, at least oneof the following:

A. Content Module;

B. Recognition Module;

C. Analysis Module;

D. Interface Layer; and

E. Data Store Layer.

Details with regards to each module is provided below. Although modulesare disclosed with specific functionality, it should be understood thatfunctionality may be shared between modules, with some functions splitbetween modules, while other functions duplicated by the modules.Furthermore, the name of the module should not be construed as limitingupon the functionality of the module. Moreover, each stage disclosedwithin each module can be considered independently without the contextof the other stages within the same module or different modules. Eachstage may contain language defined in other portions of thisspecifications. Each stage disclosed for one module may be mixed withthe operational stages of another module. In the present disclosure,each stage can be claimed on its own and/or interchangeably with otherstages of other modules.

The following depicts an example of a method of a plurality of methodsthat may be performed by at least one of the aforementioned modules.Various hardware and software components may be used at the variousstages of operations disclosed with reference to each module. Forexample, although methods may be described to be performed by a singlecomputing device, it should be understood that, in some embodiments,different operations may be performed by different networked elements inoperative communication with the computing device. For example, one ormore computing devices 900 may be employed in the performance of some orall of the stages disclosed with regard to the methods. Similarly,capturing devices 025 may be employed in the performance of some or allof the stages of the methods. As such, capturing devices 025 maycomprise at least those architectural components as found in computingdevice 900.

Furthermore, although the stages of the following example method aredisclosed in a particular order, it should be understood that the orderis disclosed for illustrative purposes only. Stages may be combined,separated, reordered, and various intermediary stages may exist.Accordingly, it should be understood that the various stages, in variousembodiments, may be performed in arrangements that differ from the onesclaimed below. Moreover, various stages may be added or removed withoutaltering or deterring from the fundamental scope of the depicted methodsand systems disclosed herein.

Consistent with embodiments of the present disclosure, a method may beperformed by at least one of the aforementioned modules. The method maybe embodied as, for example, but not limited to, executable machinecode, which when executed, performs the method.

The method may comprise the following stages or sub-stages, in noparticular order: classifying target objects for detection within a datastream; specification of target objects to be detected in the datastream; specifying alert parameters for indicating a detection of thetarget objects in the data stream; and recording other attributesderived from a detection of the target objects in the data stream,including, but not limited to, time, date, age, sex and otherattributes.

In some embodiments, the method may further comprise the stages orsub-stages of creating, maintaining, and updating target objectprofiles. Target object profiles may include a specification of aplurality of aspects used for detecting the target object in a datastream (e.g., object appearance, behaviors, time of day, and manyothers). The object profile may be created and updated at the AItraining stage during platform operation.

In various embodiments, the object profile may be universal or, in otherwords, available to more than one user of the platform, which may haveno relation to each other and be independent of one another. Forexample, a first user may be enabled to, either directly or indirectly,perform an action that causes the AI engine 100 to receive training datafor the classification of a certain target object. The target object'sprofile may be created based on the initial training. The target objectprofile may then be made available to a second user. The second user mayselect a target object for detection based on the object profile trainedfor the first user.

Furthermore, in some embodiments, the second user may then, eitherdirectly or indirectly, perform an action to re-train or otherwiseupdate the target object profile. In this way, more than one platformuser, dependent or independent, may be enabled to employ the same objectprofile and share updates in object detection training across theplatform.

In yet further embodiments, the target object profile may comprise arecommended or default set of alert parameters (e.g., AI confidence oralert threshold settings). Accordingly, a target object profile maycomprise an AI model and various alert parameters that are suggested forthe target object. In this way, a user selecting a target object may beprovided with an optimal set of alert parameters tailored to the object.These alert parameters may be determined by the platform during atraining or re-training phases associated with the target objectprofile.

Consistent with embodiments of the present disclosure, the method maycomprise the following stages or sub-stages, in no particular order:receiving multimedia content from a data stream; processing themultimedia content to detect objects within the content; and determiningwhether a detected object matches a target object.

The multimedia content may comprise, for example, but not be limited to,sensor data, such as image and/or audio data. The AI engine may, inturn, be enabled to detect objects by processing the sensor data. Theprocessing may be based on, for example, but not be limited to, acomparison of the detected objects to target object profiles. In someembodiments, additional training may occur during the analysis andresult in an update of the target object profiles.

Still consistent with embodiments of the present disclosure, the methodmay comprise the following stages or sub-stages, in no particular order:specifying at least one detection zone; associating at least one contentcapturing device with a zone; defining alert parameters for the zone;and triggering an alert for the zone upon a detection of a target objectby the AI engine.

Both the foregoing overview and the following detailed descriptionprovide examples and are explanatory only. Accordingly, the foregoingoverview and the following detailed description should not be consideredto be restrictive. Further, features or variations may be provided inaddition to those set forth herein. For example, embodiments may bedirected to various feature combinations and sub-combinations describedin the detailed description.

II. Platform Configuration

FIG. 1 illustrates one possible operating environment through which aplatform 001 consistent with embodiments of the present disclosure maybe provided. By way of non-limiting example, platform 001 may be hostedon, in part or fully, for example, but not limited to, a cloud computingservice. In some embodiments, platform 001 may be hosted on a computingdevice 900 or a plurality of computing devices 900. The variouscomponents of platform 001 may then, in turn, operate with the AI engine100 via one or more computing devices 900.

For example, an end-user 005 or an administrative user 005 may accessplatform 001 through an interface layer 015. The software applicationmay be embodied as, for example, but not be limited to, a website, a webapplication, a desktop application, and a mobile application compatiblewith a computing device 900. One possible embodiment of the softwareapplication may be provided by the HuntPro™ suite of products andservices provided by AI Concepts, LLC. As will be detailed withreference to FIG. 22 below, computing device 900 may serve to host orexecute the software application for providing an interface to operateplatform 001. The interface layer 015 may be provided to, for example,but not limited to, an end-user, an admin user. The interface layer 015may be provided on a capturing device, on a mobile device, a webapplication, or another computing device 900. The software applicationmay enable a user to interface with the AI engine 100 via, for example,a computing device 900.

Still consistent with embodiments of the present disclosure, a pluralityof content capturing devices 025 may be in operative communication withAI engine 100 and, in turn, interface with one or more users 005. Inturn, a software application on a user's device may be operative tointerface with and control the content capturing devices 025. In someembodiments, a user device may establish a direct channel in operativecommunication with the content capturing devices 025. In this way, thesoftware application may be in operative connection with a user device,a capturing device, and a computing device 900 operating the AI engine100.

Accordingly, embodiments of the present disclosure provide a softwareand hardware platform comprised of a distributed set of computingelements, including, but not limited to the following.

1. Capturing Device 025

Embodiments of the present disclosure may provide a content capturingdevice 025 for capturing and transmitting data to the AI Engine 100 forprocessing. Capturing Devices may be comprised of a multitude ofdevices, such as, but not limited to, a sensing device that isconfigured to capture and transmit optical, audio, and telemetry data.

A capturing device 025 may include, but not be limited to:

-   -   a surveillance device, such as, but not limited to:        -   motion sensor, and        -   a webcam;    -   a professional device, such as, but not limited to:        -   video camera, and        -   drone;    -   handheld device, such as, but not limited to:        -   camcorder, and        -   smart phone;    -   wearable device, such as, but not limited to:        -   helmet mounted camera, and        -   eye-glass mounted camera; and        -   a remote device, such as, but not limited to: cellular trail            camera, such as, but not limited to traditional cellular            camera and a Commander 4G LTE cellular camera; and        -   Cellular motion sensor.

Content capturing device 025 may comprise one or more of the componentsdisclosed with reference to computing device 900. In this way, capturingdevice 025 may be capable to perform various processing operations.

In some embodiments, the content capturing device 025 may comprise anintermediary device from which content is received. For example, contentfrom a capturing device 025 may be received by a computing device 900 ora cloud service with a communications module in communication with thecapturing device 025. In this way, the capturing device 025 may belimited to a short-range wireless or local area network, while theintermediary device may be in communication with AI engine 100. In otherembodiments, a communications module residing locally to the capturingdevice 025 may be enabled for communications directly with AI engine100.

Capturing devices may be operated by a user 005 of the platform 001,crowdsourced, or publicly available content feeds. Still consistent withembodiments of the present disclosure, content may be received from acontent source. The content source may comprise, for example, but not belimited to, a content publisher such as YouTube®, Facebook, or anothercontent publication platform. A user 005 may provide, for example, auniform resource locator (URL) for published content. The content may ormay not be owned or operated by a user. The platform 001 may then, inturn, be configured to access the content associated with the URL andextract the requisite data necessary for content analysis in accordanceto the embodiments of the present disclosure.

2. Data Store 020

Consistent with embodiments of the present disclosure, platform 001 maystore, for example, but not limited to, user profiles, zonedesignations, and object profiles. These stored elements, as well asothers, may all be accessible to AI engine 100 via a data store 020.

User data may include, for example, but not be limited to, a user name,email login credentials, device IDs, and other personally identifiableand non-personally identifiable data. In some embodiments, the user datamay be associated with target object classifications. In this way, eachuser 005 may have a set of target objects trained to the user's 005specifications. In additional embodiments, the object profiles may bestored by data store 020 and accessible to all platform users 005.

Zone designations may include, but not be limited to, various zones andzone parameters such as, but not limited to, device IDs, devicecoordinates, geo-fences, alert parameters, and target objects to bemonitored within the zones. In some embodiments, the zone designationsmay be stored by data store 020 and accessible to all platform users005.

3. Interface Layer 015

Embodiments of the present disclosure may provide an interface layer 015for end-users 005 and administrative users 005 of the platform 001.Interface layer 015 may be configured to allow a user 005 to interactwith the platform and to initiate and perform certain actions,configuration, monitoring, and receive alerts. Accordingly, any and alluser interaction with platform 001 may employ an embodiment of theinterface layer 015.

Interface layer 015 may provide a user interface (UI) in multipleembodiments and be implemented on any device such as, for example, butnot limited to:

-   -   Capturing Device;    -   Streaming Device;    -   Mobile device; and    -   Any other computing device 900.

The UI may consist of components/modules which enable user 005 to, forexample, configure, use, and manage capturing devices for operationwithin platform 001. Moreover, the UI may enable a user to configuremultiple aspects of platform 001, such as, but not limited to, zonedesignations, alert settings, and various other parameters operable inaccordance to the embodiments of this disclosure.

An interface layer 015 may enable an end-user to control various aspectsof platform 001. The interface layer 015 may interface directly withuser 005, as will be detailed in section (III) of this presentdisclosure. The interface layer 015 may provide the user 005 with amultitude of functions, for example, but not limited to, access to feedsfrom capturing devices, upload capability, content sourcespecifications, zone designations, target object specifications, alertparameters, training functionality, and various other settings andfeatures.

An interface layer 015 may provide alerts, which may also be referred toas notifications. The alerts may be provided to a single user{circumflex over ( )}06, or a plurality of users 005, according to theaforementioned alert parameters. The interface layer 015 and alerts mayprovide user(s) 005 access to live content streams 405. In someembodiments, the content streams 405 may be processed by the AI engine100 in real time. The AI engine 100 may also provide annotationssuperimposed over the content streams 405. The annotations may include,but are not limited to, markers over detected target objects, name ofthe detected target objects, confidence level of detection, currentdate/time/temperature, name of the zone, name associated with thecurrent capturing device 025, and any other learned feature (asillustrated in FIGS. 17-21).

In another aspect, an interface layer 015 may enable an administrativeuser 005 to control various parameters of platform 001. The interfacelayer 015 may interface directly with administrative user 005, similarto end-user, to provide control over the platform 001, as will bedetailed in section (III) of this present disclosure. Control of theplatform 001 may include, but not be limited to, maintenance, security,upgrades, user management, data management, and various other systemconfigurations and features. The interface layer 015 may be embodied ina graphical interface, command line interface, or any other UI to allowthe user 005 to interact with the platform 001.

4. AI Engine 100

Embodiments of the present disclosure may provide the AI engine 100configured to, for example, but not limited to, receive content, performrecognition methods on the content, and provide analysis, as disclosedby FIG. 2. In some embodiments, AI engine 100 may receive or output datato third party systems. Still, in some embodiments, AI engine 100 may beconfigured to provide an interface layer 015 and a data store layer 020for enabling input data streams to AI engine 100, as well as an outputprovision to third party systems and user devices from AI engine 100.Referring now to FIG. 2, embodiments of the present disclosure providean AI engine 100, within a software and/or hardware platform, comprisedof a set of modules. In some embodiments consistent with the presentdisclosure, the modules may be distributed. The modules comprise, butnot limited to:

A. Content Module 055;

B. Recognition Module 065; and

C. Analysis Module 075.

In some embodiments, the present disclosure may provide an additionalset of modules for further facilitating the software and/or hardwareplatform. The additional set of modules may comprise, but not be limitedto:

D. Interface Layer 015; and

E. Data Store Layer 020.

The aforementioned modules and functions and operations associatedtherewith may be operated by a computing device 900, or a plurality ofcomputing devices 900. In some embodiments, each module may be performedby separate, networked computing devices 900; while in otherembodiments, certain modules may be performed by the same computingdevice 900 or cloud environment. Though the present disclosure iswritten with reference to a centralized computing device 900 or cloudcomputing service, it should be understood that any suitable computingdevice 900 may be employed to provide the various embodiments disclosedherein.

Details with regards to each module is provided below. Although modulesare disclosed with specific functionality, it should be understood thatfunctionality may be shared between modules, with some functions splitbetween modules, while other functions duplicated by the modules.Furthermore, the name of the module should not be construed as limitingupon the functionality of the module. Moreover, each stage disclosedwithin each module can be considered independently without the contextof the other stages within the same module or different modules. Eachstage may contain language defined in other portions of thisspecifications. Each stage disclosed for one module may be mixed withthe operational stages of another module. In the present disclosure,each stage can be claimed on its own and/or interchangeably with otherstages of other modules.

Accordingly, embodiments of the present disclosure provide a softwareand/or hardware platform comprised of a set of computing elements,including, but not limited to, the following.

A. Content Module 055

A content module 055 may be responsible for the input of content to AIengine 100. The content may be used to, for example, perform objectdetection and tracking, or training for the purposes of object detectionand tracking. The input content may be in various forms, including, butnot limited to streaming data, received either directly or indirectlyfrom capturing devices 025. In some embodiments, capturing devices 025may be configured to provide content as a live feed, either directly byway of a wired or wireless connection, or through an intermediary deviceas described above. In other embodiments, the content may be static orprerecorded.

In various embodiments, capturing devices 025 may be enabled to transmitcontent to AI engine 100 only upon an active state of content detection.For example, should capturing devices 025 not detect any change in thecontent being captured, AI engine 100 may not need to receive and/orprocess the same content. When, however, a change in the content isdetected (e.g., motion is detected within the frame of a capturingdevice), then the content may be transmitted. As will be understood by aperson having ordinary skill in the art with various embodiments of thepresent disclosure, the transmission of content may be controlled on aper capturing device 025 and adjusted by the user 005 of the platform001.

Still consistent with embodiments of the present disclosure, the contentmodule 055 may provide uploaded content directly to AI engine 100. Aswill be described with reference to interface layer 015, the platform001 may enable the user 005 to upload content to the AI engine 100. Thecontent may be embodied in various forms (e.g., videos, images, andsensor data) and uploaded for the purposes of, but not limited to,training the AI engine 100 or detecting and tracking target objects bythe AI engine 100.

In further embodiments, the content module 055 may receive content froma content source. The content source may be, for example, but notlimited to, a data store 020 (e.g., local data store 020 or third-partydata store 020) or a content stream 405 from a third-party platform. Forexample, as previously mentioned, the platform 001 may enable the user005 to specify a content source with a URL. In turn, the content module055 may be configured to access the URL and retrieve the content to beprocessed by AI engine 100. In some embodiments, the URL may point to awebpage or another source that contains one or more content streams 405.Still consistent with the present disclosure, the content module 055 maybe configured to parse the data from the sources and inputs for one ormore content streams 405 to be processed by the AI engine 100.

B. Recognition Module 065

A recognition module 065 may be responsible for the recognition and/ortracking of target objects within the content provided by a contentmodule 055. The recognition module 065 may comprise a data store 020from which to access target object data. The target object data may beused to compare against detected objects in the content to determine ifan object within the content matches a target object.

In some embodiments, data store layer 020 may store the requisite dataof target objects and detection parameters. Accordingly, recognitionmodule 065 may be configured to retrieve or receive content from contentmodule 055 and perform recognition based on a comparison of the contentto object data retrieved from data store layer 020.

Further still, in some embodiments, the data store layer 020 may beprovided by, for example, but not limited to, an external system oftarget object definitions. In this way, AI engine 100 performsprocessing on content received from an external system in order torecognize objects based on parameters provided by the same or anothersystem.

AI engine 100 may be configured to trigger certain events upon therecognition of a target object by recognition module 065 (e.g., alerts).The events may be defined by settings specified by a user 005. In someembodiments, data store layer 020 may store the various event parametersconfigured by the user 005. As will be detailed below, the eventparameters may be tied to different target object classifications and/ordifferent zones and/or different events. One such example is to triggera notification when a detected object matches a male moose present inzone 3 for over 5 minutes.

FIG. 3 illustrates one example of an AI engine 100 architecture forperforming object recognition. In various embodiments, the architecturemay be comprised of, but not limited to, an input stage 085, arecognition, tracking, and learning stage 090, and an output stage 095.Accordingly, AI engine 100 may receive or retrieve data from contentmodule 055 during an input stage. The content 085 may then be processedin accordance to target object classifications associated with thecontent. The target object classifications may be based on, for example,but not limited to, the zone with which the content is associated.Associating content with a zone, and defining target objects to betracked within a zone, will be detailed with reference to FIGS. 6 and 7,FIG. 11, FIG. 12, and FIG. 13.

Upon receiving the content 085, AI engine 100 may proceed to recognitionstage 090. In this stage, AI engine 100 may employ the given content andprocess the content through, for example, a neural net 094 for detectionof learned features 092 associated with the target objects. In this way,AI engine may, for example, compare the content with learned features092 associated with the target object to determine if a target object isdetected within the content. It should be noted that, while the input(s)may be provided to AI engine 100, neural net 094 and learned features092 associated with target objects may be trained and processedinternally. In another embodiment, the learned features may be retrievedby the AI engine 100 from a separate data store layer 020 provided by aseparate system.

Consistent with embodiments of the present disclosure, the learnedfeatures 092 may be provided to the AI engine 100 via training methodsand procedures as will be detailed with reference to FIGS. 4 and 5, FIG.10, and FIGS. 17-21. In some embodiments, the acquired training data andlearned features 092 may reside at, for example, data store layer 020.The features may be related to various target objects types for which AIengine 100 was trained, such as, but not limited to, animals, people,vehicles, and various other animate and inanimate objects.

For each target object type, AI engine 100 may be trained to detectdifferent species, models, and features of each object. By way ofnon-limiting example, learned features 092 for an animal target objecttype may include a body type of an animal, a stance of an animal, awalking/running/galloping pattern of the animal, and horns of an animal.

In various embodiments, neural net 094 may be employed in the trainingof learned features 092, as well recognition stage 090 in the detectionof learned features 092. As will be detailed below, the more trainingthat AI engine 100 undergoes, the higher chance target objects may bedetected, and with a higher confidence level of detection. Thus, themore users use AI engine 100, the more content AI engine 100 has withwhich to train, resulting in a greater list of target objects, types,and corresponding features. Furthermore, the more content the AI engine100 processes, the more the AI engine 100 trains itself, makingdetection more accurate with higher confidence level.

Accordingly, neural net 094 may detect target objects within contentreceived or retrieved in input stage 085. By way of non-limitingexample, recognition stage 090 may perform AI based algorithms foranalyzing detected objects within the content for behavioral patterns,motion patterns, visual cues, object curvatures, geo-locations, andvarious other parameters that may correspond to the learned features092. In this way, target objects may be recognized within the content.

Having detected a target object, AI engine 100 may proceed to outputstage 095. The output may be, for example, an alert sent to interfacelayer 015. In some embodiments, the output may be, for example, anoutput sent to analysis module 075 for ascertaining furthercharacteristics of the detected target object.

C. Analysis Module 075

Consistent with some embodiments of the present disclosure, once adetected object has been classified to correspond to a target object,additional analysis may be performed. For example, the combination offeatures associated with the target object may be further analyzed toascertain particular aspects of the detected target object. Thoseaspects may include, for example, but not be limited to, a health of ananimal, an age of an animal, a gender of an animal, and a score for ananimal.

As will be detailed below, these aspects of the target object may beused in determining whether or not to provide an alert. For example, ifa designated zone is configured to only issue alerts when a targetobject, such as a deer, with a certain score (e.g., based on, forexample, the animal's horns), then analysis module 075 may be employedto calculate a score for each target object detected that matches a deertarget object and is within the designated zone.

Still consistent with the present disclosure, other aspects may includethe detection of Chronic Wasting Disease (CWD). As CWD spreads in wildcervid populations, platform 001 may be employed as a broad remotesurveillance system for detecting infected populations. Accordingly, AIengine may be trained with images and video footage of both healthy andCWD infected animals. In this way, AI engine 100 may determine thefeatures inherent to deer infected with CWD. In turn, platform 001 maybe configured to monitor vast amounts of content from a plurality ofcontent sources (e.g., social media, SD cards, trail cameras, and otherinput data provided by content module 055). Upon detection, platform 001may be configured to track infected animals and alert appropriateintervention teams to zones in which these infected animals weredetected. FIG. 14 illustrates one example of a user interface forproviding a CWD alert. The platform 001 may provide tracking of theinfected animal, even across zones, to help intervention teams find theanimal.

Furthermore, the analysis module 075 consistent with the presentdisclosure may detect any feature it was trained to detect, where thefeature may be recognized by means of visual analysis, behavioralanalysis, auditory analysis, or analysis of any other aspect where thedata is provided about that aspect. While the examples provided hereinmay relate to animals, specifically cervid, it should be understood thatthe platform 001 is target object agnostic. Any animate or inanimateobject may be detected, and any aspect of such object may be analyzed,provided that the platform 001 received training data for theobject/aspect.

D. Interface Layer 015

Embodiments of the present disclosure may provide an interface layer 015for end-users 005 and administrative users 005 of the platform 001.Interface layer 015 may be configured to allow a user 005 to interactwith the platform and to initiate and perform certain actions, such as,but not limited to, configuration, monitoring, and receive alerts.Accordingly, any and all user interaction with platform 001 may employan embodiment of the interface layer 015.

Interface layer 015 may provide a user interface (UI) in multipleembodiments and be implemented on any device such as, for example, butnot limited to:

-   -   Capturing Device;    -   Streaming Device;    -   Mobile device; and    -   Any other computing device 900.

The UI may consist of components/modules which enable user 005 to, forexample, configure, use, and manage capturing devices 025 for operationwithin platform 001. Moreover, the UI may enable a user to configuremultiple aspects of platform 001, such as, but not limited to, zonedesignations, alert settings, and various other parameters operable inaccordance to the embodiments of this disclosure.

An interface layer 015 may enable an end-user to control various aspectsof platform 001. The interface layer 015 may interface directly withuser 005, as will be detailed in section (III) of this presentdisclosure. The interface layer 015 may provide the user 005 with amultitude of functions, for example, but not limited to, access to feedsfrom capturing devices, upload capability, content sourcespecifications, zone designations, target object specifications, alertparameters, training functionality, and various other settings andfeatures.

An interface layer 015 may provide alerts, which may also be referred toas notifications. The alerts may be provided to a single user{circumflex over ( )}06, or a plurality of users 005, according to theaforementioned alert parameters. The interface layer 015 and alerts mayprovide user(s) 005 access to live content streams 405. In someembodiments, the content streams 405 may be processed by the AI engine100 in real time. The AI engine 100 may also provide annotationssuperimposed over the content streams 405. The annotations may include,but are not limited to, markers over detected target objects, name ofthe detected target objects, confidence level of detection, currentdate/time/temperature, name of the zone, name associated with thecurrent capturing device 025, and any other learned feature (asillustrated in FIGS. 17-21).

In another aspect, an interface layer 015 may enable an administrativeuser 005 to control various parameters of platform 001. The interfacelayer 015 may interface directly with administrative user 005, similarto end-user, to provide control over the platform 001, as will bedetailed in section (III) of this present disclosure. Control of theplatform 001 may include, but not be limited to, maintenance, security,upgrades, user management, data management, and various other systemconfigurations and features. The interface layer 015 may be embodied ina graphical interface, command line interface, or any other UI to allowthe user 005 to interact with the platform 001.

Furthermore, interface layer 015 may comprise an Application ProgrammingInterface (API) module for system-to-system communication of input andoutput data into and out of the platform 001 and between variousplatform 001 components (e.g., AI engine 100). By employing an APImodule, platform 001 and/or various components therein (e.g., AI engine100) may be integrated into external systems. For example, externalsystems may perform certain function calls and methods to send data intoAI engine 100 as well as receive data from AI engine 100. In this way,the various embodiments disclosed with reference to AI engine 100 may beused modularly with other systems.

Still consistent with the present disclosure, in some embodiments, theAPI may allow automation of certain tasks which may otherwise requirehuman interaction. The API allows a script/program to perform tasksexposed to a user 005 in an automated fashion. Applicationscommunicating through the API can not only reduce the workload for auser 005 by means of automation and can also react faster than ispossible for a human.

Furthermore, the API provides different ways of interaction with theplatform 001, consistent with the present disclosure. This may enablethird parties to develop their own interface layers 015, such as, butnot limited to, a graphical user interface (GUI) for an iPhone orraspberry pi. In a similar fashion, the API allows integration withdifferent smart systems, such as, but not limited to, smart homesystems, and smart assistants, such as but not limited to, google homeand Alexa.

The API may provide a plurality of embodiments consistent with thepresent disclosure, for example, but not limited to, a RESTful APIinterface and JSON. The data may be passed over a TCT/UDP directcommunication, tunneled over SSH or VPN, or over any other networkingtopology.

The API can be accessed over a multitude of mediums, for example, butnot limited to, fiber, direct terminal connection, and other wired andwireless interfaces.

Further still, the nodes accessing the API can be in any embodiment of acomputing device 900, for example, but not limited to, a mobile device,a server, a raspberry pi, an embedded device, a fully programmable gatearray (FPGA), a cloud service, a laptop, and a server. The instructionsperforming API calls can be in any form compatible with a computingdevice 900, such as, but not limited to, a script, a web application, acompiled application, a macro, and software as a service (SaaS) cloudservice, and machine code.

E. Data Store Layer

Consistent with embodiments of the present disclosure, platform 001 maystore, for example, but not limited to, user profiles, zonedesignations, and target object profiles. These stored elements, as wellas others, may all be accessible to AI engine 100 via a data store 020.

User data may include, for example, but not be limited to, a user name,email, logon credentials, device IDs, and other personally identifiableand non-personally identifiable data. In some embodiments, the user datamay be associated with target object classifications. In this way, eachuser 005 may have a set of target objects trained to the user's 005specifications. In additional embodiments, the object profiles may bestored by data store 020 and accessible to all platform users 005.

Zone designations may include, but not be limited to, various zones andzone parameters such as, but not limited to, device IDs, devicecoordinates, geo-fences, alert parameters, and target objects to bemonitored within the zones. In some embodiments, the zone designationsmay be stored by data store 020 and accessible to all platform users005.

III. Platform Operation

Embodiments of the present disclosure provide a hardware and softwareplatform 001 operative by a set of methods and computer-readable storagecomprising instructions configured to operate the aforementioned modulesand computing elements in accordance with the methods. The followingdepicts an example of a method of a plurality of methods that may beperformed by at least one of the aforementioned modules. Varioushardware components may be used at the various stages of operationsdisclosed with reference to each module.

For example, although methods may be described to be performed by asingle computing device 900, it should be understood that, in someembodiments, different operations may be performed by differentnetworked computing devices 900 in operative communication. For example,cloud service and/or plurality of computing devices 900 may be employedin the performance of some or all of the stages disclosed with regard tothe methods. Similarly, capturing device 025 may be employed in theperformance of some or all of the stages of the methods. As such,capturing device 025 may comprise at least a portion of thearchitectural components comprising the computing device 900.

Furthermore, even though the stages of the following example method aredisclosed in a particular order, it should be understood that the orderis disclosed for illustrative purposes only. Stages may be combined,separated, reordered, and various intermediary stages may exist.Accordingly, it should be understood that the various stages, in variousembodiments, may be performed in arrangements that differ from the onesclaimed below. Moreover, various stages may be added or removed from thewithout altering or deterring from the fundamental scope of the depictedmethods and systems disclosed herein.

Consistent with embodiments of the present disclosure, a method may beperformed by at least one of the aforementioned modules. The method maybe embodied as, for example, but not limited to, computer instructions,which when executed, perform the method. The method may comprise thefollowing stages:

-   -   receiving a content stream from a content source, the content        source comprising at least one of the following:    -   a capturing device, and    -   a uniform resource locator;    -   establishing at least one target object to detect within the        content stream, wherein establishing the at least one target        object to detect comprises:    -   retrieving at least one target object profile from a database of        learned target object profiles, wherein the at least one learned        target object profile is associated with the at least one target        object to detect, and wherein the database of learned target        object profiles is associated with target objects that have been        trained for detection within at least one frame of the content        stream, and    -   analyzing at least one frame associated with the content stream,        wherein analyzing the at least one frame comprises:    -   detecting, employing a neural net, the at least one target        object within the at least one frame by matching aspects of the        at least one frame to aspects of the at least one learned target        object profile;    -   establishing at least one parameter for communicating target        object detection related data, wherein the at least one        parameter specifies the following:    -   at least one aspect of the at least one detected target object,        and    -   at least one aspect of the content source; and    -   communicating the target object detection related data when the        at least one parameter is met, wherein communicating the target        object detection related data comprises at least one of the        following:    -   transmitting the at least one frame along with annotations        associated with the detected at least one target object; and    -   transmitting a notification comprising the target object        detection related data.

Still consistent with embodiments of the present disclosure, an AIEngine may be provided. The AI engine may comprise, but not be limitedto, for example, a content module, a recognition module, and an analysismodule.

The content module may be configured to receive a content stream from atleast one content source.

The recognition module may be configured to:

match aspects of the content stream to at least one learned targetobject profile from a database of learned target object profiles todetect target objects within the content, and upon a determination thatat least one of the detected target objects corresponds to the at leastone learned target object profile:

classify the at least one detected target object based on the at leastone learned target object profile, and

update the at least one learned target object profile with at least oneaspect of the at least one detected target object.

The analysis module may be configured to:

process the at least one detected target object through a neural net fora detection of learned features associated with the at least onedetected target object, wherein the learned features are specified bythe at least one learned target object profile associated with the atleast one detected target object,

determine, based on the process, the following:

a gender of the at least one detected target object,

an age of the at least one detected target object,

a health of the at least one detected target object, and

a score for the at least one detected target object, and

update the learned target object profile with the detected learnedfeatures.

In yet further embodiments of the present disclosure, a systemcomprising at least one capturing device, at least one end-user device,and an AI engine may be provided.

The least one capturing device may be configured to:

register with an AI engine,

capture at least one of the following:

visual data, and

audio data,

digitize the captured data, and

transmit the digitized data as at least one content stream to the AIengine.

The at least one end-user device may be configured to:

configure the at least one capturing device to be in operativecommunication with the AI engine,

define at least one zone, wherein the at least one end-user device beingconfigured to define the at least one zone comprises the at least oneend-user device being configured to:

specify at least one content source for association with the at leastone zone, and

specify the at least one content stream associated with the at least onecontent source, the specified at least one content stream to beprocessed by the AI engine for the at least one zone,

specify at least one zone parameter from a plurality of zone parametersfor the at least one zone, wherein the zone parameters comprise:

a plurality of selectable target object designations for detectionwithin the at least one zone, the target object designations beingassociated with a plurality of learned target object profiles trained bythe AI engine,

specify at least one alert parameter from a plurality of alertparameters for the at least one zone, wherein the alert parameterscomprise:

triggers for an issuance of an alert,

recipients that receive the alert,

actions to be performed when an alert is triggered, and

restrictions on issuing the alert,

receive the alert from the AI engine, and

display the detected target object related data associated with thealert, wherein the detected target object related data comprises atleast one frame from the at least one content stream.

The AI engine of the system may comprise a content module, a recognitionmodule, an analysis module, and an interface layer.

The content module may be configured to receive the content stream fromthe at least one capturing device.

The recognition module may be configured to:

match aspects of the content stream to at least one learned targetobject profile in a database of the plurality of learned target objectprofiles trained by the AI engine to detect target objects within thecontent, and upon a determination that at least one of the detectedtarget objects corresponds to the at least one learned target objectprofile:

classify the at least one detected target object based on the at leastone learned target object profile, and

update the at least one learned target object profile with at least oneaspect of the at least one detected target object;

an analysis module configured to:

process the at least one detected target object through a neural net fora detection of learned features associated with the at least onedetected target object, wherein the learned features are specified bythe at least one learned target object profile associated with the atleast one detected target object,

determine, based on the process, the following attributes of the atleast one detected target object:

a gender of the at least one detected target object,

an age of the at least one detected target object,

a health of the at least one detected target object, and

a score for the at least one detected target object,

update the learned target object profile with the detected learnedfeatures, and

determine whether the at least one detected target object corresponds toat least one of the target object designations associated with the zonespecified at the end-user device, and

determine whether the attributes associated with the at least onedetected object correspond to the triggers for the issuance of thealert.

The interface layer may be configured to:

communicate the detected target object data to the at least one end-userdevice, wherein the detected target object related data comprises atleast one of the following:

at least one frame along with annotations associated with the detectedat least one target object, and

a push notification to the at least one end-user device.

A. AI Training

AI Engine 100 may be trained in accordance to, but not limited to, themethods illustrated in FIG. 4 and FIG. 5. AI Engine 100 may be trainedto recognize various target objects and establish learned features 092for various target objects. Training methods may be required for the AIEngine 100 to determine which aspects of an object to assess in objectsdetected within content supplied by content module 055. Accordingly,each trained target object model may be embodied as a target objectprofile in data layer 020. In some embodiments, the trained models canthen be used platform wide, for all users, as a universal target objectmodel.

Training enables AI Engine 100 to, among many functions, properlyclassify input(s) (e.g., content received from content module 055).Furthermore, training methods may be required to ascertain which outputsare useful for the user 005, and when to provide them. Training can beinitiated by the user(s), as well as triggered automatically by thesystem itself. Although embodiments of the present disclosure refer tovisual content, similar methods and systems may be employed for thepurposes of training other content types, such as, but not limited to,ultrasonic/audio content, infrared (IR) content, ultraviolet (UV)content and content comprised of magnetic readings.

1) Receiving Training Content 105

In a first stage, a training method may begin by receiving content fortraining purposes. Content may be received from content module 055during a training input stage 085. In some embodiments consistent withthe present disclosure, the recognition state 090 may trigger a trainingmethod and provide that training method content into the input stage085.

-   -   a. The received training content may be received from a        capturing device 025, such as, but not limited to:        -   i. a surveillance device;        -   ii. a professional device;        -   iii. handheld device;        -   iv. wearable device;        -   v. a remote device, such as, but not limited to:    -   a. cellular trail camera, such as, but not limited to:        -   i. traditional cellular camera and        -   ii. a Commander 4G LTE cellular camera, and    -   b. Cellular motion sensor        -   vi. intermediary platform such as, but not limited to:            -   1. computing device 900, and            -   2. cloud computing device.

The training content may be selected to be the same or similar to whatAI engine 100 is likely to find during recognition stage 090. Forexample, if a user 005 elects to train AI engine 100 to detect a deer,training content will consist of pictures of deer. Accordingly, trainingcontent may be curated for the specific training user 005 desires toachieve. In some embodiments, AI engine 100 may filter the content toremove any unwanted objects or artifacts, or otherwise enhance quality,whether still or in motion, in order to better detect the target objectsselected by user 005 for training.

-   -   b. The training content may contain images in different        conditions, such as, but not limited to:        -   i. Varying Quality

AI engine 100 may encounter content of various quality due to equipmentand condition variations, such as, for example, but not limited to:

-   -   1. High Resolution (FIG. 17);    -   2. Low Resolution (FIG. 18);    -   3. Large Objects (FIG. 17);    -   4. Small Objects (FIG. 020);    -   5. Color Objects (FIG. 17); and    -   6. Monochrome/Infrared (FIG. 18-21).        -   ii. Varying Environmental Backgrounds

AI engine 100 may encounter different weather conditions that must beaccounted for, such as, but not limited to:

-   -   1. Foggy (FIG. 19);    -   2. Rainy;    -   3. Snowy;    -   4. Day (FIG. 17);    -   5. Night (FIG. 19-21);    -   6. Indoor; and    -   7. Outdoor (FIG. 17-21).        -   iii. Varying Layouts

The training images may comprise variations to the positioning andlayout of the target objects within a frame. In this way, AI engine 100may learn how to identify objects in different positions and layoutswithin an environment, such as, but not limited to:

-   -   1. Small Background Objects (FIG. 020);    -   2. Overlapped Objects (FIG. 21);    -   3. Large foreground objects (FIG. 17);    -   4. Multiple Objects (FIG. 020);    -   5. Single Objects (FIGS. 17-18);    -   6. Partially out of frame (FIG. 18);    -   7. Doppler effect.        -   iv. Varying Parameters

The training images may depict target objects with varying parameters.In this way, the AI engine {circumflex over ( )}112 may learn thedifferent parameters associated with the target objects, such as, forexample, but not limited to:

-   -   1. Age;    -   2. Sex;    -   3. Size;    -   4. Score;    -   5. Disease;    -   6. Type;    -   7. Color;    -   8. Logo; and    -   9. Behavior.

2) Classifying Training Content 110

Once the training images are received, AI engine 100 may be trained tounderstand a context in which it will be training for target objectdetection. Accordingly, in some embodiments, content classificationsprovided by user 005 may be provided in furtherance of this stage. Theclassifications may be provided along with the training data by way ofinterface layer 015. In various embodiments, the classification data maybe integrated with the training data as, for example, but not limitedto, metadata. Content classification may inform the AI engine 100 aswhat is represented in each image.

-   -   a. Content may be classified by class, such as, but not limited        to:        -   i. Type of animate object, such as, but not limited to:            -   1. Type of Animal (such as protected animals), such as,                but not limited to:                -   a. Deer (FIG. 17-21);                -   b. Human;                -   c. Pig;                -   d. Fish; and                -   e. Bird            -   2. Type of plant such as, for example, but not limited                to:                -   a. Rose;                -   b. Oak;                -   c. Tree; and                -   d. Flower.        -   ii. Type of inanimate object such as, but not limited to:            -   1. Type of vehicle;            -   2. Type of drone; and            -   3. Type of robot.

Furthermore, AI engine 100 may be trained to detect certaincharacteristics of target objects in order to, for example, ascertainadditional aspects of detected objects (e.g., a particular sub-groupingof the target object).

-   -   b. Content classifications may be refined by, such as, but not        limited to:        -   i. Gender;        -   ii. Race;        -   iii. Age;        -   iv. Health; and        -   v. Score.    -   c. Content may be further classified by features of Target        Objects, such as, but not limited to:        -   i. Tattoos;        -   ii. Birthmarks;        -   iii. Tags;        -   iv. License Plate; and        -   v. Other Markings.    -   d. Content may also be classified by a symbol, image, or textual        content demarking an origin, such as, but not limited to:        -   i. UPS;        -   ii. Fed-Ex;        -   iii. Ford;        -   iv. Kia;        -   v. Apple;        -   vi. leopard print;        -   vii. tessellation;        -   viii. fractal;        -   ix. Calvin Klein; and        -   x. Hennessy.    -   e. Content may be classified by Identity such as, but not        limited to:        -   i. John Doe;        -   ii. Jane Smith;        -   iii. Donald Trump;        -   iv. Next door neighbor;        -   v. Mail man; and        -   vi. Neighbor's cat.

The aforementioned examples are diversified to indicate, in anon-limiting way, the variety of target objects that AI engine 100 canbe trained to detect. Furthermore, as will be detailed below, platform001 may be programmed with certain rules for including or excludingcertain target objects when triggering outputs (e.g., alerts). Forexample, user 005 may wish to be alerted when a person approaches theirfront door but would like to exclude alerts if that person is, forexample, a mail man.

3) Normalizing Training Content 115

In some embodiments, due to varying factors that may be present in thetraining content (e.g., environmental conditions), AI engine 100 maynormalize the training content. Normalization may be performed in orderto minimize the impact of the varying factors. Normalization may beaccomplished using various techniques, such as, but not limited to:

a. Red eye reduction;

b. Brightness normalization;

c. Contrast normalization;

d. Hue adjustment; and

e. Noise reduction.

In various embodiments, AI engine 100 may undergo the stage ofidentifying and extracting objects within the training content (e.g.,object detection). For example, AI engine 100 may be provided withtraining content that comprises one or more objects in one or moreconfigurations. Once the objects are detected within the content, adetermination that the objects are to be classified as indicated may bemade.

4) Transferring Learning from the Previous Model 120

In various embodiments of the present disclosure, AI engine 100 mayemploy a baseline from which to start content evaluation. For thisbaseline, a previously configured evaluation model may be used. Theprevious model may be retrieved from, for example, data layer 020. Insome embodiments, a previous model may not be employed on the very firsttraining pass.

5) Making Evaluation Predictions 125

At a making evaluation predictions 125 stage, AI engine 100 may beconfigured to process the training data. Professing the data may be usedto, for example, train the AI engine 100. During certain iterations, AIengine 100 may be configured to evaluate the AI engine's 100 precision.Here, rather than processing training data, AI engine 100 may processevaluation data to evaluate the performance of the trained model.Accordingly, AI engine 100 may be configured to make predictions andtest the prediction's accuracy.

-   -   a. Embodiments of the present disclosure may use “live” data to        train and evaluate the model used by AI engine 100.

In this instance, AI engine 100 may receive live data from contentmodule 055. Accordingly, AI engine 100 may perform one or more of thefollowing operations: receive the content, normalize it, and makepredictions based on a current or previous model. Furthermore, in oneaspect, AI engine 100 may use the content to train a new model (e.g., animproved model) should the content be used as training data or evaluatecontent via the current or previous training model. In turn, theimproved model may be used for evaluation on the next pass, if required.

-   -   b. Embodiments of the present disclosure may use pre-recorded        and/or rendered training data to train and evaluate the model        used by AI engine 100.

In this instance, the AI engine 100 may be trained with any content,such as, but not limited to, previously captured content. Herein, sincethe content is not streamed to AI engine 100 as a live feed, AI engine100 may not require training in real time. This may provide foradditional training opportunities and, therefore, lead to more effectivetraining. This may also allow training on less powerful equipment or useless resources to train.

In some embodiments, AI engine 100 may randomly choose which predictionsto send for evaluation by an external source. The external source maybe, for example, a human (e.g., sent via interface layer 015) or anothertrained model (e.g., sent via interface layer 015). In turn, theexternal source may validate or invalidate the predictions received fromthe AI engine 100.

6) Calculating the Precision of the Evaluation 130

Consistent with embodiments of the present disclosure, the AI engine 100may proceed to a subsequent stage in training to calculate howaccurately it can evaluate objects within the content to identify theobjects' correct classification. Referring back, AI engine 100 may beprovided with training content that comprises one or more objects in oneor more configurations. Once the objects are detected within thecontent, a determination that the objects are to be classified asindicated may be made. The precision of this determination may becalculated. The precision may be determined in combination between humanverification and evaluation data. In some embodiments consistent withthe present disclosure, a percentage of the verified training data maybe reserved for testing the evaluation accuracy of the AI engine 100.

In some embodiments, prior to training, a user 005 may set targetprecision, or minimum accuracy of the AI engine 100. For example, the AIengine 100 may be unable to determine its precision without ambiguity.At this stage, an evaluation may be made if the desired accuracy hasbeen reached. For example, AI engine 100 may provide the predictionresults for evaluation by an external source. The external source maybe, for example, a human (e.g., sent via interface layer 015) or anothertrained model (e.g., sent via interface layer 015). In turn, theexternal source may validate or invalidate the predictions received fromAI engine 100.

B. Zone Designation

FIG. 13 illustrates one example of a method for establishing a contentsource for a zone designation. Although zoning may not be necessary inplatform 001, it may help a user 005 organize various content sources.Accordingly, embodiments of the present disclosure may provide zonedesignations to enable the assignment of a plurality of content streams405 to the same detection, alert parameters, location, and/or any othergrouping a user 005 may choose. Nevertheless, in some embodiments, thetracking and alert parameters associated with one or more contentsources within a zone may be customized to differ from other parametersin the same zone. Zone designation may be performed as follows:

1. Configuring at Least One Capturing Device 205

In an initial stage, a user 005 may register a content source withplatform 001. This stage may be performed at the content source itself.In such instance, the content source is may be in operativecommunication with platform 001, via for example, an API module.Accordingly, in some embodiments, the content source may be adapted withinterface layer 015. Interface layer 015 may enable a user 005 toconnect content source to platform 001 such that it may be operativewith AI engine 100. This process may be referred to as pairing,registration, or configuration, and may be performed, as mentionedabove, through an intermediary device.

Consistent with embodiments of the present disclosure, the contentsource might not be owned or operated by the user 005. Rather, the user005 may be enabled to select third party content sources, such as, butnot limited to:

-   -   a. Public cameras; and    -   b. Security cameras.

Accordingly, content sources need not be traditional capturing devices.Rather, content platforms may be employed, such as, for example, but notlimited by:

-   -   a. Social media platform and/or feed;    -   b. YouTube video;    -   c. Hunter Submission;    -   d. Solid state media, such as SD Card;    -   e. Optical media, such as DVD; and    -   f. A website.

Furthermore, each source may be designated with certain labels. Thelabels may correspond to, for example, but not be limited by, a name, asource location, a device type, and various other parameters.

2. Providing and Receiving Content Stream 405 Selection 210 and 215

Having configured one or more contents sources, platform 001 may then beenabled to access the content associated with each content source. FIG.11 illustrates one example of a UI that may be provided by interfacelayer 015. The content may be, for example, but not limit to, a contentstream 405 received from a configured capturing device 025. Metadata 410associated with content stream 405 may be provided in some embodiments.In other embodiments, the content may be comprised of a data streamreceived from a content source, but not limited to, such as a live feedmade accessible online. Whatever it's form, the content may be providedto a user 005 for selection and further configuration. Next, a user 005may select one or more content streams 405 for designation as a zone.

3. Designated Content Streams as a Zone 220

Selected content streams 405 may be designated as a detection and alertzone. It should be noted that, while a selection of content streams 405was used to designate a detection and alert zone, a designation of thezone is possible with or without content stream 405 selection. Forexample, in some embodiments, the designation may be based on aselection of capturing devices. In yet further embodiments, a zone maybe, for example, an empty container and, subsequent to the establishmentof a zone, content sources may be attributed to the zone.

Each designated zone may be associated with, for example, but notlimited to, a storage location in data layer 020. The zone may beprivate or public. Furthermore, one or more users 005 may be enabled toattribute their content source to a zone, thereby adding a number ofcontent sources being processed for target object detection and/ortracking in a zone. In instances where more than one user 005 has accessto a zone, one or more administrative users 005 may be designated toregulate the roles and permissions associated with the zone.

Accordingly, a zone may be a group of one or more content sources. Thecontent sources may be obtained from, for example, the content module055. For example, the content source may be one or more capturingdevices 025 positioned throughout a particular geographical location.Here, each zone may represent a physical location associated with thecapturing devices 025. In some embodiments, the capturing devices 025may provide location information associated with its position. In turn,on or more capturing devices 025 within a proximity to each other may bedesignated to be within the same zone.

Still consistent with embodiments of the present disclosure, zones neednot be associated with a location. For example, zones can be groupingsof content sources that are to be tracked for the same target objects.However, the groupings may refer to geo-zones, although a physicallocation is not tracked. For example, zones may be grouped by, but notbe limited to:

-   -   Living Room    -   Outdoor Sector 1    -   Indoor Sector 1    -   Backyard    -   Driveway    -   Office Building    -   Shed    -   Grand Canyon

The aforementioned examples of zones may be associated with contentsources in accordance to the method of FIG. 13. By way of non-limitingexample, a first plurality of content capturing devices 025 may be setup around a first geographical region, and a second plurality of contentcapturing devices 025 may be set up around a second geographical region.In some embodiments, platform 001 may suggest grouping the capturingdevices 025 based on a location indication received by each of thecapturing devices 025. In further embodiments, platform 001 may enable auser 005 to select capturing devices 025 and designate them to begrouped within the zone.

Each zone may be designated with certain labels. The labels maycorrespond to, for example, but not be limited by, a name, a sourcelocation, a device type, storage location, and various other parameters.Moreover, each content source may also contain identifying labels.

Consistent with embodiments of the present disclosure, platform 001 maybe operative to perform the following operations: generating at leastone content stream 405; capturing data associated with the at least onecontent stream 405; aggregating the data as metadata to the at least onecontent stream 405; transmitting the at least one content stream 405 andthe associated metadata; receiving a plurality of content streams 405and the associated metadata; organizing the plurality of content streams405, wherein organizing the plurality of content streams 405 comprises:establishing a multiple stream container 420 for grouping capturedcontent streams of the plurality of content streams 405 based onmetadata associated with the captured content streams 405, wherein themultiple stream container 420 is established subsequent to receivingcontent for the multiple stream container 420, wherein establishing themultiple stream container 420 comprises: i) receiving a specification ofparameters for content streams 405 to be grouped into the multiplestream container 420, wherein the parameters are configured tocorrespond to data points within the metadata associated with thecontent streams 405, and wherein receiving the specification of theparameters further comprises receiving descriptive header dataassociated with the criteria, the descriptive header data being used todisplay labels associated with the multiple content streams 405.

FIG. 12 illustrates how one or more content streams 405 may beassociated with a zone. Grouping content streams 405 into a container420 may be based on, at least in part, parameters defined for themultiple stream parameter, and metadata data associated with the contentstreams 405. The content streams 405 may be labeled, wherein labelingthe content within the multiple stream container 420 comprises, but isnot limited to, at least one of the following: identifiers associatedwith the content source; a location of capture associated with eachcontent source, such as, but not limited to, a venue, place, event; atime of capture associated with each content stream 405, such as, butnot limited to, a date, start-time, end-time, duration; and orientationdata associated with each content stream 405. In some embodiments,labeling the content streams 405 further comprises labeling the multiplestream container 420 based on parameters and descriptive headerassociated with the multiple stream container 420. The labeled contentstreams 405 may then be indexed, searched, and discovered by otherplatform users.

C. Target Object Detection and Alert Parameters

Content obtained from content sources may be processed by the AI engine100 for target object detection. Although zoning is not necessary on theplatform 001, it may help a user 005 organize various content sourceswith the same target object detection and alert parameters, or the samegeographical location. Accordingly, embodiments of the presentdisclosure may provide zone designations to enable the assignment of aplurality of content streams 405 to the same detection and alertparameters. Nevertheless, in some embodiments, the tracking and alertparameters associated with one or more content sources within a zone maybe customized to differ from other parameters in the same zone.

1. Receiving Zone Designation 220

Detection and alert parameters may be received via an interface layer015. FIG. 13 illustrates one example of a UI for specifying alertparameters. Accordingly, in some embodiments, aforementioned parametersmay be defined upon a selection of a zone to which they may beassociated with. Thus, a user 005 may select which zone(s), to configureone or more alert parameters associated with the aforementioned zone(s).

2. Specifying Alert Parameters 225

An interface layer 015 consistent with embodiments of the presentdisclosure may enable a user 005 to configure parameters that trigger analert for one or more defined zones. As target objects are detected, theplatform facilitates real-time transmission of intelligent alerts.Alerts can be transmitted to and received on any computing device 900such as, but not limited to, a mobile device, laptop, desktop, and anyother computing device 900.

In some embodiments, the computing device 900 that receives the alertsmay also be the content capturing device 025 that sends the content foranalysis to the AI engine 100. For example, a user 005 may have awearable device with content capturing means. The captured content maybe analyzed for any desired target objects specified by the user 005. Inturn, when a desired target object is detected within the content stream405, the wearable device may receive the corresponding alert as definedby the aforementioned user 005. Furthermore, alerts can be transmittedand received over any medium, such as, but not limited to e-mail, SMS,website and mobile device push notifications.

In various embodiments, an API module may be employed to pushnotifications to external systems. FIG. 14 illustrates on example ofalert notifications. The notifications may be custom notifications withuser-defined messaging, that may include relevant content, a confidencescore, and various other parameters (e.g., the aforementioned contentsource metadata). Furthermore, the notifications may comprise a livefeed of the detected target object that triggered the alert as it isbeing tracked through the zone. By way of non-limiting example,notifications may report different alert parameters, such as, forexample, but not limited to:

a. Target Object detected

-   -   1. Frequency of the detected Target Object

b. Time and duration detected

c. Location detected

d. Sensor (or Source) detected

e. Action Triggered (if any)

Parameters that may trigger an alert to be sent may comprise, forexample, but not limited to, the following:

-   -   a. Monitoring Time Period        -   Example Command: Limit Alerts to triggers received within or            outside a specified time period.    -   b. Group size        -   Example Command: Trigger an alert if the number of detected            targets is greater than, equal to and/or less than            specified.    -   c. Score        -   Example Command: Trigger an alert if the score of the            detected target is greater than, less than, and/or equal to            the score specified.    -   d. Age        -   Example Command: Trigger an alert if the age of the target            is greater than, less than, and/or equal to the age            specified.    -   e. Gender        -   Example Command: Trigger an alert if the gender of the            detected target matches the gender specified.    -   f. Disease        -   Example Command: Trigger an alert if the detected target is            found to carry or be free from a specified disease.    -   g. Geo location        -   Example Command: Trigger an alert if the target enters            and/or leaves a specified location.    -   h. Content source        -   Example Command: Trigger an alert based on the content            source type or other content source related parameters.    -   i. Confidence level        -   Example Command: Trigger an alert if the confidence level is            greater than, less than, and/or equal to the confidence            level specified, wherein, the confidence threshold can be            adjusted separately for every target that triggers an alert.    -   j. Perform Action        -   Example Command: Trigger an action to be performed, for            example, but not limited to:            -   i. Send Target Object data to the Training Method,            -   ii. Upload picture to cloud storage, and            -   iii. Notify Law Enforcement    -   k. Recipient/Medium        -   Example Command: Each alert parameter can trigger an alert            to be sent to plurality of recipients over a plurality of            medium(s).

Consistent with embodiments of the present disclosure, alert parametersmay define destinations for the alerts. For example, a first type ofalert may be transmitted to a first user 005, a second type of alert maybe transmitted to a second user 005, and a third type of alert may betransmitted to both first and second users 005. The alert destinationsmay be based on any alert parameter, and the detected target object.Accordingly, alerts may be customized based on target object types aswell as other alert parameters (e.g., content source).

In some embodiments, the interface layer 015 may provide a user 005 withoperative controls associated with the content source, the zone in whichthe content source is located, and any other integrated peripheraldevices (e.g., a trap; a remote detonation; a lock; a siren; or aactivate a command on a capturing device 025 associated with the sourcesuch as, but not limited to, an operation of the capturing device 025).Accordingly, an action to be triggered upon an alert may be defined as aparameter associated with the zone, content source and/or target object.

3. Specifying Target Objects for Tracking 230

Embodiments of the present disclosure may enable a user 005 to definetarget objects to be tracked for each content source and/or zone. Insome embodiments, a user 005 may select a target object from an objectlist populated by platform 001. The object list may be obtained from allthe models the AI engine 100 has trained, by any user 005. Crowdsourcing training from each user's 005 usage of public object trainingof target objects may improve target object recognition for all platformusers 005.

In some embodiments, however, object profiles may remain private andlimited to one or more users 005. User 005 may be enabled to define acustom target object, and undergo AI engine 100 training, as disclosedherein, or otherwise.

Furthermore, as a user 005 may specify target objects to trigger alerts,so may a user 005 specify target objects to exclude from triggeringalerts. In this way, a user 005 may not be notified if any otherwisedetected object matches a target object list.

4. Activating Zone Monitoring 235

Having defined the parameters for tracking target objects, platform 001may now begin monitoring content sources for the defined target objects.In some embodiments, a user 005 may enable or disable monitoring by zoneor content source. Once enabled, the interface layer 015 may provide aplurality functions with regard to each monitored zone.

For example, a user 005 may be enabled to monitor the AI engine 100 inreal time, review historical data, and make modifications. The interfacelayer 015 may expose a user 005 to a multitude of data points andactions, for example, but not limited to, viewing any stream in realtime (FIG. 15) and reviewing recognized target objects (FIG. 16). Sincethe platform 001 keeps a record of every recognized target object, auser 005 can review this record and associated metadata, such as, butnot limited to:

-   -   A. Time of event;    -   B. Category of target;    -   C. Geo-location of target; and    -   D. Target parameters.

Furthermore, since the platform 001 keeps track of the target objects, auser 005 may follow each target object in real time. For example, upon adetection of a tracked object within a first content source (e.g., afirst camera), platform 001 may be configured to display each contentsource in which the target object is currently active (eithersynchronously or sequentially switching as the target object travelsfrom one content source to the next). In some embodiments, the platform001 may calculate and provide statistics about the target objects beingtracked, for example, but not limited to:

-   -   A. Time of day target is most likely to be detected;    -   B. Most likely location of target;    -   C. Proportion of males to females of a specific animal Target        Object;    -   D. Average speed of the Target Object; and    -   E. Distribution of ages of Target Objects.

Still consistent with embodiments of the present disclosure, a user 005may designate select content to be sent back to AI engine 100 forfurther training.

D. Target Object Recognition

FIGS. 8-9 illustrate methods for target object recognition. In thesemethods, the platform 001 may receive inputs from content module 055,processes them with the AI Engine 100 to perform target objectrecognition, then provide a user 005 with the outputs as indicated in,for example, FIG. 3.

1. Receiving Content from Content Source 305

In a first stage, AI engine 100 may receive content from content module055. The content may be received from, for example, but not limited to,configured capturing devices, streams, or uploaded content.

2. Performing Content Recognition 310

Consistent with embodiments of the present disclosure, AI engine 100 maybe trained to recognize objects from a content source. Object detectionmay be based on a universal set of objects for which AI engine 100 hasbeen trained, whether or not defined to be tracked within the designatedzone associated with the content source.

3. Generating List of Detected Target Objects 315

As target objects are detected, AI engine 100 may generate a list ofdetected target objects. In some embodiments consistent with the presentdisclosure, all objects and trained attributes may be recorded, whetherthey are specifically targeted or not. Furthermore, in certain cases,the detected objects may be sent back for feedback loop review 350, asillustrated in the method in FIG. 10.

4. Retrieving List of Target Objects 320

AI engine 100 may then compare the list of detected target objects tothe specified target objects to track and/or generate alerts for withregard to an associated content source or zone.

5. Checking Fora Match 325

When a match has been detected, the platform 001 may trigger thedesignated alert for the content source or zone. This may include astoring of the content source data at, for example, the data layer 020.The data may comprise, for example, but not limited to, a capture of astill frame, or a sequence of frames in a video format with theassociated metadata.

In some embodiments, the content may then be provided to a user 005. Forexample, platform 001 may notify interested parties and/or provide thedetected content to the interested parties at a stage 335. That is,platform 001 may enable a user 005 to access content detected in realtime through the monitoring systems, the interface layer 015, andmethods disclosed herein.

6. Recording Object Classifications 330

AI engine 100 may record detected classified target objects in the datalayer 020. FIG. 10 discloses one method of integrating target objecttraining during the target object recognition process and may referenceback to the feedback loop indicated in FIG. 4.

IV. Computing Device Architecture

Platform 001 may be embodied as, for example, but not be limited to, awebsite, a web application, a desktop application, backend application,and a mobile application compatible with a computing device 900. Thecomputing device 900 may comprise, but not be limited to the following:

-   -   Mobile computing device such as, but is not limited to, a        laptop, a tablet, a smartphone, a drone, a wearable, an embedded        device, a handheld device, an Arduino, an industrial device, or        a remotely operable recording device;    -   A supercomputer, an exa-scale supercomputer, a mainframe, or a        quantum computer;    -   A minicomputer, wherein the minicomputer computing device        comprises, but is not limited to, an IBM AS400/iSeries/System I,        A DEC VAX/PDP, a HP3000, a Honeywell-Bull DPS, a Texas        Instruments TI-990, or a Wang Laboratories VS Series;    -   A microcomputer, wherein the microcomputer computing device        comprises, but is not limited to, a server, wherein a server may        be rack mounted, a workstation, an industrial device, a        raspberry pi, a desktop, or an embedded device;

Platform 001 may be hosted on a centralized server or a cloud computingservice. Although methods have been described to be performed by acomputing device 900, it should be understood that, in some embodiments,different operations may be performed by a plurality of the computingdevices 900 in operative communication over one or more networks.

Embodiments of the present disclosure may comprise a system having acentral processing unit (CPU) 920, a bus 930, a memory unit 940, a powersupply unit (PSU) 950, and one or more Input/Output (I/O) units. The CPU920 coupled to the memory unit 940 and the plurality of I/O units 960via the bus 930, all of which are powered by the PSU 950. It should beunderstood that, in some embodiments, each disclosed unit may actuallybe a plurality of such units for the purposes of redundancy, highavailability, and/or performance. The combination of the presentlydisclosed units is configured to perform the stages any method disclosedherein.

FIG. 22 is a block diagram of a system including computing device 900.Consistent with an embodiment of the disclosure, the aforementioned CPU920, the bus 930, the memory unit 940, a PSU 950, and the plurality ofI/O units 960 may be implemented in a computing device, such ascomputing device 900 of FIG. 22. Any suitable combination of hardware,software, or firmware may be used to implement the aforementioned units.For example, the CPU 920, the bus 930, and the memory unit 940 may beimplemented with computing device 900 or any of other computing devices900, in combination with computing device 900. The aforementionedsystem, device, and components are examples and other systems, devices,and components may comprise the aforementioned CPU 920, the bus 930, thememory unit 940, consistent with embodiments of the disclosure.

One or more computing devices 900 may be embodied as any of thecomputing elements illustrated in FIG. {circumflex over ( )}A and 2,including, but not limited to, Capturing Devices 025, Data Store 020,Interface Layer 015 such as User and Admin interfaces, RecognitionModule 065, Content Module 055, Analysis Module 075 and neural net. Acomputing device 900 does not need to be electronic, nor even have a CPU920, nor bus 930, nor memory unit 940. The definition of the computingdevice 900 to a person having ordinary skill in the art is “A devicethat computes, especially a programmable [usually] electronic machinethat performs high-speed mathematical or logical operations or thatassembles, stores, correlates, or otherwise processes information.” Anydevice which processes information qualifies as a computing device 900,especially if the processing is purposeful.

With reference to FIG. 22, a system consistent with an embodiment of thedisclosure may include a computing device, such as computing device 900.In a basic configuration, computing device 900 may include at least oneclock module 910, at least one CPU 920, at least one bus 930, and atleast one memory unit 940, at least one PSU 950, and at least one I/O960 module, wherein I/O module may be comprised of, but not limited to anon-volatile storage sub-module 961, a communication sub-module 962, asensors sub-module 963, and a peripherals sub-module 964.

A system consistent with an embodiment of the disclosure the computingdevice 900 may include the clock module 910 may be known to a personhaving ordinary skill in the art as a clock generator, which producesclock signals. Clock signal is a particular type of signal thatoscillates between a high and a low state and is used like a metronometo coordinate actions of digital circuits. Most integrated circuits(ICs) of sufficient complexity use a clock signal in order tosynchronize different parts of the circuit, cycling ata rate slower thanthe worst-case internal propagation delays. The preeminent example ofthe aforementioned integrated circuit is the CPU 920, the centralcomponent of modern computers, which relies on a clock. The onlyexceptions are asynchronous circuits such as asynchronous CPUs. Theclock 910 can comprise a plurality of embodiments, such as, but notlimited to, single-phase clock which transmits all clock signals oneffectively 1 wire, two-phase clock which distributes clock signals ontwo wires, each with non-overlapping pulses, and four-phase clock whichdistributes clock signals on 4 wires.

Many computing devices 900 use a “clock multiplier” which multiplies alower frequency external clock to the appropriate clock rate of the CPU920. This allows the CPU 920 to operate at a much higher frequency thanthe rest of the computer, which affords performance gains in situationswhere the CPU 920 does not need to wait on an external factor (likememory 940 or input/output 960). Some embodiments of the clock 910 mayinclude dynamic frequency change, where, the time between clock edgescan vary widely from one edge to the next and back again.

A system consistent with an embodiment of the disclosure the computingdevice 900 may include the CPU unit 920 comprising at least one CPU Core921. A plurality of CPU cores 921 may comprise identical the CPU cores921, such as, but not limited to, homogeneous multi-core systems. It isalso possible for the plurality of CPU cores 921 to comprise differentthe CPU cores 921, such as, but not limited to, heterogeneous multi-coresystems, big.LITTLE systems and some AMD accelerated processing units(APU). The CPU unit 920 reads and executes program instructions whichmay be used across many application domains, for example, but notlimited to, general purpose computing, embedded computing, networkcomputing, digital signal processing (DSP), and graphics processing(GPU). The CPU unit 920 may run multiple instructions on separate CPUcores 921 at the same time. The CPU unit 920 may be integrated into atleast one of a single integrated circuit die and multiple dies in asingle chip package. The single integrated circuit die and multiple diesin a single chip package may contain a plurality of other aspects of thecomputing device 900, for example, but not limited to, the clock 910,the CPU 920, the bus 930, the memory 940, and I/O 960.

The CPU unit 921 may contain cache 922 such as, but not limited to, alevel 1 cache, level 2 cache, level 3 cache or combination thereof. Theaforementioned cache 922 may or may not be shared amongst a plurality ofCPU cores 921. The cache 922 sharing comprises at least one of messagepassing and inter-core communication methods may be used for the atleast one CPU Core 921 to communicate with the cache 922. The inter-corecommunication methods may comprise, but not limited to, bus, ring,two-dimensional mesh, and crossbar. The aforementioned CPU unit 920 mayemploy symmetric multiprocessing (SMP) design.

The plurality of the aforementioned CPU cores 921 may comprise softmicroprocessor cores on a single field programmable gate array (FPGA),such as semiconductor intellectual property cores (IP Core). Theplurality of CPU cores 921 architecture may be based on at least one of,but not limited to, Complex instruction set computing (CISC), Zeroinstruction set computing (ZISC), and Reduced instruction set computing(RISC). At least one of the performance-enhancing methods may beemployed by the plurality of the CPU cores 921, for example, but notlimited to Instruction-level parallelism (ILP) such as, but not limitedto, superscalar pipelining, and Thread-level parallelism (TLP).

Consistent with the embodiments of the present disclosure, theaforementioned computing device 900 may employ a communication systemthat transfers data between components inside the aforementionedcomputing device 900, and/or the plurality of computing devices 900. Theaforementioned communication system will be known to a person havingordinary skill in the art as a bus 930. The bus 930 may embody internaland/or external plurality of hardware and software components, forexample, but not limited to a wire, optical fiber, communicationprotocols, and any physical arrangement that provides the same logicalfunction as a parallel electrical bus. The bus 930 may comprise at leastone of, but not limited to a parallel bus, wherein the parallel buscarry data words in parallel on multiple wires, and a serial bus,wherein the serial bus carry data in bit-serial form. The bus 930 mayembody a plurality of topologies, for example, but not limited to, amultidrop/electrical parallel topology, a daisy chain topology, and aconnected by switched hubs, such as USB bus. The bus 930 may comprise aplurality of embodiments, for example, but not limited to:

-   -   Internal data bus (data bus) 931/Memory bus    -   Control bus 932    -   Address bus 933    -   System Management Bus (SMBus)    -   Front-Side-Bus (FSB)    -   External Bus Interface (EBI)    -   Local bus    -   Expansion bus    -   Lightning bus    -   Controller Area Network (CAN bus)    -   Camera Link    -   ExpressCard    -   Advanced Technology management Attachment (ATA), including        embodiments and derivatives such as, but not limited to,        Integrated Drive Electronics (IDE)/Enhanced IDE (EIDE), ATA        Packet Interface (ATAPI), Ultra-Direct Memory Access (UDMA),        Ultra ATA (UATA)/Parallel ATA (PATA)/Serial ATA (SATA),        CompactFlash (CF) interface, Consumer Electronics ATA        (CE-ATA)/Fiber Attached Technology Adapted (FATA), Advanced Host        Controller Interface (AHCI), SATA Express (SATAe)/External SATA        (eSATA), including the powered embodiment eSATAp/Mini-SATA        (mSATA), and Next Generation Form Factor (NGFF)/M.2.    -   Small Computer System Interface (SCSI)/Serial Attached SCSI        (SAS)    -   HyperTransport    -   InfiniBand    -   RapidIO    -   Mobile Industry Processor Interface (MIPI)    -   Coherent Processor Interface (CAPI)    -   Plug-n-play    -   1-Wire    -   Peripheral Component Interconnect (PCI), including embodiments        such as, but not limited to, Accelerated Graphics Port (AGP),        Peripheral Component Interconnect eXtended (PCI-X), Peripheral        Component Interconnect Express (PCI-e) (i.e., PCI Express Mini        Card, PCI Express M.2 [Mini PCIe v2], PCI Express External        Cabling [ePCIe], and PCI Express OCuLink [Optical Copper{Cu}        Link]), Express Card, AdvancedTCA, AMC, Universal 10,        Thunderbolt/Mini DisplayPort, Mobile PCIe (M-PCIe), U.2, and        Non-Volatile Memory Express (NVMe)/Non-Volatile Memory Host        Controller Interface Specification (NVMHCIS).    -   Industry Standard Architecture (ISA) including embodiments such        as, but not limited to Extended ISA (EISA),        PC/XT-bus/PC/AT-bus/PC/104 bus (e.g., PC/104-Plus,        PCI/104-Express, PCI/104, and PCI-104), and Low Pin Count (LPC).    -   Music Instrument Digital Interface (MIDI)    -   Universal Serial Bus (USB) including embodiments such as, but        not limited to, Media Transfer Protocol (MTP)/Mobile        High-Definition Link (MHL), Device Firmware Upgrade (DFU),        wireless USB, InterChip USB, IEEE 1394 Interface/Firewire,        Thunderbolt, and eXtensible Host Controller Interface (xHCI).

Consistent with the embodiments of the present disclosure, theaforementioned computing device 900 may employ hardware integratedcircuits that store information for immediate use in the computingdevice 900, know to the person having ordinary skill in the art asprimary storage or memory 940. The memory 940 operates at high speed,distinguishing it from the non-volatile storage sub-module 961, whichmay be referred to as secondary or tertiary storage, which providesslow-to-access information but offers higher capacities at lower cost.The contents contained in memory 940, may be transferred to secondarystorage via techniques such as, but not limited to, virtual memory andswap. The memory 940 may be associated with addressable semiconductormemory, such as integrated circuits consisting of silicon-basedtransistors, used for example as primary storage but also other purposesin the computing device 900. The memory 940 may comprise a plurality ofembodiments, such as, but not limited to volatile memory, non-volatilememory, and semi-volatile memory. It should be understood by a personhaving ordinary skill in the art that the ensuing are non-limitingexamples of the aforementioned memory:

-   -   Volatile memory which requires power to maintain stored        information, for example, but not limited to, Dynamic        Random-Access Memory (DRAM) 941, Static Random-Access Memory        (SRAM) 942, CPU Cache memory 925, Advanced Random-Access Memory        (A-RAM), and other types of primary storage such as        Random-Access Memory (RAM).    -   Non-volatile memory which can retain stored information even        after power is removed, for example, but not limited to,        Read-Only Memory (ROM) 943, Programmable ROM (PROM) 944,        Erasable PROM (EPROM) 945, Electrically Erasable PROM (EEPROM)        946 (e.g., flash memory and Electrically Alterable PROM        [EAPROM]), Mask ROM (MROM), One Time Programable (OTP) ROM/Write        Once Read Many (WORM), Ferroelectric RAM (FeRAM), Parallel        Random-Access Machine (PRAM), Split-Transfer Torque RAM        (STT-RAM), Silicon Oxime Nitride Oxide Silicon (SONOS),        Resistive RAM (RRAM), Nano RAM (NRAM), 3D XPoint, Domain-Wall        Memory (DWM), and millipede memory.    -   Semi-volatile memory which may have some limited non-volatile        duration after power is removed but loses data after said        duration has passed. Semi-volatile memory provides high        performance, durability, and other valuable characteristics        typically associated with volatile memory, while providing some        benefits of true non-volatile memory. The semi-volatile memory        may comprise volatile and non-volatile memory and/or volatile        memory with battery to provide power after power is removed. The        semi-volatile memory may comprise, but not limited to        spin-transfer torque RAM (STT-RAM).

Consistent with the embodiments of the present disclosure, theaforementioned computing device 900 may employ the communication systembetween an information processing system, such as the computing device900, and the outside world, for example, but not limited to, human,environment, and another computing device 900. The aforementionedcommunication system will be known to a person having ordinary skill inthe art as I/O 960. The I/O module 960 regulates a plurality of inputsand outputs with regard to the computing device 900, wherein the inputsare a plurality of signals and data received by the computing device900, and the outputs are the plurality of signals and data sent from thecomputing device 900. The I/O module 960 interfaces a plurality ofhardware, such as, but not limited to, non-volatile storage 961,communication devices 962, sensors 963, and peripherals 964. Theplurality of hardware is used by the at least one of, but not limitedto, human, environment, and another computing device 900 to communicatewith the present computing device 900. The I/O module 960 may comprise aplurality of forms, for example, but not limited to channel I/O,port-mapped I/O, asynchronous I/O, and Direct Memory Access (DMA).

Consistent with the embodiments of the present disclosure, theaforementioned computing device 900 may employ the non-volatile storagesub-module 961, which may be referred to by a person having ordinaryskill in the art as one of secondary storage, external memory, tertiarystorage, off-line storage, and auxiliary storage. The non-volatilestorage sub-module 961 may not be accessed directly by the CPU 920without using intermediate area in the memory 940. The non-volatilestorage sub-module 961 does not lose data when power is removed and maybe two orders of magnitude less costly than storage used in memorymodule, at the expense of speed and latency. The non-volatile storagesub-module 961 may comprise a plurality of forms, such as, but notlimited to, Direct Attached Storage (DAS), Network Attached Storage(NAS), Storage Area Network (SAN), nearline storage, Massive Array ofIdle Disks (MAID), Redundant Array of Independent Disks (RAID), devicemirroring, off-line storage, and robotic storage. The non-volatilestorage sub-module (961) may comprise a plurality of embodiments, suchas, but not limited to:

-   -   Optical storage, for example, but not limited to, Compact        Disk (CD) (CD-ROM/CD-R/CD-RW), Digital Versatile Disk (DVD)        (DVD-ROM/DVD-R/DVD+R/DVD-RW/DVD+RW/DVD±RW/DVD+R        DL/DVD-RAM/HD-DVD), Blu-ray Disk (BD) (BD-ROM/BD-R/BD-RE/BD-R        DL/BD-RE DL), and Ultra-Density Optical (UDO).    -   Semiconductor storage, for example, but not limited to, flash        memory, such as, but not limited to, USB flash drive, Memory        card, Subscriber Identity Module (SIM) card, Secure Digital (SD)        card, Smart Card, CompactFlash (CF) card, and Solid State Drive        (SSD) and memristor.    -   Magnetic storage such as, but not limited to, Hard Disk Drive        (HDD), tape drive, carousel memory, and Card Random-Access        Memory (CRAM).    -   Phase-change memory    -   Holographic data storage such as Holographic Versatile Disk        (HVD)    -   Molecular Memory    -   Deoxyribonucleic Acid (DNA) digital data storage

Consistent with the embodiments of the present disclosure, theaforementioned computing device 900 may employ the communicationsub-module 962 as a subset of the I/O 960, which may be referred to by aperson having ordinary skill in the art as at least one of, but notlimited to, computer network, data network, and network. The networkallows computing devices 900 to exchange data using connections, whichmay be known to a person having ordinary skill in the art as data links,between network nodes. The nodes comprise network computer devices 900that originate, route, and terminate data. The nodes are identified bynetwork addresses and can include a plurality of hosts consistent withthe embodiments of a computing device 900. The aforementionedembodiments include, but not limited to personal computers, phones,servers, drones, and networking devices such as, but not limited to,hubs, switches, routers, modems, and firewalls.

Two nodes can be said are networked together, when one computing device900 is able to exchange information with the other computing device 900,whether or not they have a direct connection with each other. Thecommunication sub-module 962 supports a plurality of applications andservices, such as, but not limited to World Wide Web (WWW), digitalvideo and audio, shared use of application and storage computing devices(900), printers/scanners/fax machines, email/online chat/instantmessaging, remote control, distributed computing, etc. The network maycomprise a plurality of transmission mediums, such as, but not limitedto conductive wire, fiber optics, and wireless. The network may comprisea plurality of communications protocols to organize network traffic,wherein application-specific communications protocols are layered, maybe known to a person having ordinary skill in the art as carried aspayload, over other more general communications protocols. The pluralityof communications protocols may comprise, but not limited to, IEEE 802,ethernet, Wireless LAN (WLAN/Wi-Fi), Internet Protocol (IP) suite (e.g.,TCP/IP, UDP, Internet Protocol version 4 [IPv4], and Internet Protocolversion 6 [IPv6]), Synchronous Optical Networking (SONET)/SynchronousDigital Hierarchy (SDH), Asynchronous Transfer Mode (ATM), and cellularstandards (e.g., Global System for Mobile Communications [GSM], GeneralPacket Radio Service [GPRS], Code-Division Multiple Access [CDMA], andIntegrated Digital Enhanced Network [IDEN]).

The communication sub-module 962 may comprise a plurality of size,topology, traffic control mechanism and organizational intent. Thecommunication sub-module 962 may comprise a plurality of embodiments,such as, but not limited to:

-   -   Wired such as, but not limited to, coaxial cable, phone lines,        twisted pair cables (ethernet), and InfiniBand.    -   Wireless communications such as, but not limited to,        communications satellites, cellular systems, radio        frequency/spread spectrum technologies, IEEE 802.11 Wi-Fi,        Bluetooth, NFC, free-space optical communications, terrestrial        microwave, and Infrared (IR) communications. Wherein cellular        systems embody technologies such as, but not limited to, 3G,4G        (such as WiMax and LTE), and 5G.    -   Parallel communications such as, but not limited to, LPT ports    -   Serial communications such as, but not limited to, RS-232 and        USB    -   Fiber Optic communications such as, but not limited to,        Single-mode optical fiber (SMF) and Multi-mode optical fiber        (MMF).    -   Power Line communications

The aforementioned network may comprise a plurality of layouts, such as,but not limited to, bus network such as ethernet, star network such asWi-Fi, ring network, mesh network, fully connected network, and treenetwork. The network can be characterized by its physical capacity orits organizational purpose. Use of the network, including userauthorization and access rights, differ accordingly. Thecharacterization may include, but not limited to nanoscale network,Personal Area Network (PAN), Local Area Network (LAN), Home Area Network(HAN), Storage Area Network (SAN), Campus Area Network (CAN), backbonenetwork, Metropolitan Area Network (MAN), Wide Area Network (WAN),enterprise private network, Virtual Private Network (VPN), and GlobalArea Network (GAN).

Consistent with the embodiments of the present disclosure, theaforementioned computing device 900 may employ the sensors sub-module963 as a subset of the I/O 960. The sensors sub-module 963 comprises atleast one of the devices, modules, and subsystems whose purpose is todetect events or changes in its environment and send the information tothe computing device 900. Sensors are sensitive to the measuredproperty, are not sensitive to any property not measured, but may beencountered in its application, and do not significantly influence themeasured property. The sensors sub-module 963 may comprise a pluralityof digital devices and analog devices, wherein if an analog device isused, an Analog to Digital (A-to-D) converter must be employed tointerface the said device with the computing device 900. The sensors maybe subject to a plurality of deviations that limit sensor accuracy. Thesensors sub-module 963 may comprise a plurality of embodiments, such as,but not limited to, chemical sensors, automotive sensors,acoustic/sound/vibration sensors, electric current/electricpotential/magnetic/radio sensors,environmental/weather/moisture/humidity sensors, flow/fluid velocitysensors, ionizing radiation/particle sensors, navigation sensors,position/angle/displacement/distance/speed/acceleration sensors,imaging/optical/light sensors, pressure sensors, force/density/levelsensors, thermal/temperature sensors, and proximity/presence sensors. Itshould be understood by a person having ordinary skill in the art thatthe ensuing are non-limiting examples of the aforementioned sensors:

-   -   Chemical sensors such as, but not limited to, breathalyzer,        carbon dioxide sensor, carbon monoxide/smoke detector, catalytic        bead sensor, chemical field-effect transistor, chemiresistor,        electrochemical gas sensor, electronic nose,        electrolyte-insulator-semiconductor sensor, energy-dispersive        X-ray spectroscopy, fluorescent chloride sensors, holographic        sensor, hydrocarbon dew point analyzer, hydrogen sensor,        hydrogen sulfide sensor, infrared point sensor, ion-selective        electrode, nondispersive infrared sensor, microwave chemistry        sensor, nitrogen oxide sensor, olfactometer, optode, oxygen        sensor, ozone monitor, pellistor, pH glass electrode,        potentiometric sensor, redox electrode, zinc oxide nanorod        sensor, and biosensors (such as nanosensors).    -   Automotive sensors such as, but not limited to, air flow        meter/mass airflow sensor, air-fuel ratio meter, AFR sensor,        blind spot monitor, engine coolant/exhaust gas/cylinder        head/transmission fluid temperature sensor, hall effect sensor,        wheel/automatic transmission/turbine/vehicle speed sensor,        airbag sensors, brake fluid/engine crankcase/fuel/oil/tire        pressure sensor, camshaft/crankshaft/throttle position sensor,        fuel/oil level sensor, knock sensor, light sensor, MAP sensor,        oxygen sensor (02), parking sensor, radar sensor, torque sensor,        variable reluctance sensor, and water-in-fuel sensor.    -   Acoustic, sound and vibration sensors such as, but not limited        to, microphone, lace sensor (guitar pickup), seismometer, sound        locator, geophone, and hydrophone.    -   Electric current, electric potential, magnetic, and radio        sensors such as, but not limited to, current sensor, Daly        detector, electroscope, electron multiplier, faraday cup,        galvanometer, hall effect sensor, hall probe, magnetic anomaly        detector, magnetometer, magnetoresistance, MEMS magnetic field        sensor, metal detector, planar hall sensor, radio direction        finder, and voltage detector.    -   Environmental, weather, moisture, and humidity sensors such as,        but not limited to, actinometer, air pollution sensor,        bedwetting alarm, ceilometer, dew warning, electrochemical gas        sensor, fish counter, frequency domain sensor, gas detector,        hook gauge evaporimeter, humistor, hygrometer, leaf sensor,        lysimeter, pyranometer, pyrgeometer, psychrometer, rain gauge,        rain sensor, seismometers, SNOTEL, snow gauge, soil moisture        sensor, stream gauge, and tide gauge.    -   Flow and fluid velocity sensors such as, but not limited to, air        flow meter, anemometer, flow sensor, gas meter, mass flow        sensor, and water meter.    -   Ionizing radiation and particle sensors such as, but not limited        to, cloud chamber, Geiger counter, Geiger-Muller tube,        ionization chamber, neutron detection, proportional counter,        scintillation counter, semiconductor detector, and        thermoluminescent dosimeter.    -   Navigation sensors such as, but not limited to, air speed        indicator, altimeter, attitude indicator, depth gauge, fluxgate        compass, gyroscope, inertial navigation system, inertial        reference unit, magnetic compass, MHD sensor, ring laser        gyroscope, turn coordinator, variometer, vibrating structure        gyroscope, and yaw rate sensor.    -   Position, angle, displacement, distance, speed, and acceleration        sensors such as, but not limited to, accelerometer, displacement        sensor, flex sensor, free fall sensor, gravimeter, impact        sensor, laser rangefinder, LIDAR, odometer, photoelectric        sensor, position sensor such as GPS or Glonass, angular rate        sensor, shock detector, ultrasonic sensor, tilt sensor,        tachometer, ultra-wideband radar, variable reluctance sensor,        and velocity receiver.    -   Imaging, optical and light sensors such as, but not limited to,        CMOS sensor, colorimeter, contact image sensor, electro-optical        sensor, infra-red sensor, kinetic inductance detector, LED as        light sensor, light-addressable potentiometric sensor, Nichols        radiometer, fiber-optic sensors, optical position sensor,        thermopile laser sensor, photodetector, photodiode,        photomultiplier tubes, phototransistor, photoelectric sensor,        photoionization detector, photomultiplier, photoresistor,        photoswitch, phototube, scintillometer, Shack-Hartmann,        single-photon avalanche diode, superconducting nanowire        single-photon detector, transition edge sensor, visible light        photon counter, and wavefront sensor.    -   Pressure sensors such as, but not limited to, barograph,        barometer, boost gauge, bourdon gauge, hot filament ionization        gauge, ionization gauge, McLeod gauge, Oscillating U-tube,        permanent downhole gauge, piezometer, Pirani gauge, pressure        sensor, pressure gauge, tactile sensor, and time pressure gauge.    -   Force, Density, and Level sensors such as, but not limited to,        bhangmeter, hydrometer, force gauge/force sensor, level sensor,        load cell, magnetic level/nuclear density/strain gauge,        piezocapacitive pressure sensor, piezoelectric sensor, torque        sensor, and viscometer.    -   Thermal and temperature sensors such as, but not limited to,        bolometer, bimetallic strip, calorimeter, exhaust gas        temperature gauge, flame detection/pyrometer, Gardon gauge,        Golay cell, heat flux sensor, microbolometer, microwave        radiometer, net radiometer, infrared/quartz/resistance        thermometer, silicon bandgap temperature sensor, thermistor, and        thermocouple.    -   Proximity and presence sensors such as, but not limited to,        alarm sensor, doppler radar, motion detector, occupancy sensor,        proximity sensor, passive infrared sensor, reed switch, stud        finder, triangulation sensor, touch switch, and wired glove.

Consistent with the embodiments of the present disclosure, theaforementioned computing device 900 may employ the peripheralssub-module 962 as a subset of the I/O 960. The peripheral sub-module 964comprises ancillary devices uses to put information into and getinformation out of the computing device 900. There are 3 categories ofdevices comprising the peripheral sub-module 964, which exist based ontheir relationship with the computing device 900, input devices, outputdevices, and input/output devices. Input devices send at least one ofdata and instructions to the computing device 900. Input devices can becategorized based on, but not limited to:

-   -   Modality of input such as, but not limited to, mechanical        motion, audio, and visual.    -   Whether the input is discrete, such as but not limited to,        pressing a key, or continuous such as, but not limited to        position of a mouse.    -   The number of degrees of freedom involved such as, but not        limited to, two-dimensional mice vs three-dimensional mice used        for Computer-Aided Design (CAD) applications.

Output devices provide output from the computing device 900. Outputdevices convert electronically generated information into a form thatcan be presented to humans. Input/output devices perform that performboth input and output functions. It should be understood by a personhaving ordinary skill in the art that the ensuing are non-limitingembodiments of the aforementioned peripheral sub-module 964:

-   -   Input Devices        -   Human Interface Devices (HID), such as, but not limited to,            pointing device (e.g., mouse, touchpad, joystick,            touchscreen, game controller/gamepad, remote, light pen,            light gun, Wii remote, jog dial, shuttle, and knob),            keyboard, graphics tablet, digital pen, gesture recognition            devices, magnetic ink character recognition, Sip-and-Puff            (SNP) device, and Language Acquisition Device (LAD).        -   High degree of freedom devices, that require up to six            degrees of freedom such as, but not limited to, camera            gimbals, Cave Automatic Virtual Environment (CAVE), and            virtual reality systems.        -   Video Input devices are used to digitize images or video            from the outside world into the computing device 900. The            information can be stored in a multitude of formats            depending on the user's requirement. Examples of types of            video input devices include, but not limited to, digital            camera, digital camcorder, portable media player, webcam,            Microsoft Kinect, image scanner, fingerprint scanner,            barcode reader, 3D scanner, laser rangefinder, eye gaze            tracker, computed tomography, magnetic resonance imaging,            positron emission tomography, medical ultrasonography, TV            tuner, and iris scanner.        -   Audio input devices are used to capture sound. In some            cases, an audio output device can be used as an input            device, in order to capture produced sound. Audio input            devices allow a user to send audio signals to the computing            device 900 for at least one of processing, recording, and            carrying out commands. Devices such as microphones allow            users to speak to the computer in order to record a voice            message or navigate software. Aside from recording, audio            input devices are also used with speech recognition            software. Examples of types of audio input devices include,            but not limited to microphone, Musical Instrumental Digital            Interface (MIDI) devices such as, but not limited to a            keyboard, and headset.        -   Data AcQuisition (DAQ) devices covert at least one of analog            signals and physical parameters to digital values for            processing by the computing device 900. Examples of DAQ            devices may include, but not limited to, Analog to Digital            Converter (ADC), data logger, signal conditioning circuitry,            multiplexer, and Time to Digital Converter (TDC).    -   Output Devices may further comprise, but not be limited to:        -   Display devices, which convert electrical information into            visual form, such as, but not limited to, monitor, TV,            projector, and Computer Output Microfilm (COM). Display            devices can use a plurality of underlying technologies, such            as, but not limited to, Cathode-Ray Tube (CRT), Thin-Film            Transistor (TFT), Liquid Crystal Display (LCD), Organic            Light-Emitting Diode (OLED), MicroLED, and Refreshable            Braille Display/Braille Terminal.        -   Printers such as, but not limited to, inkjet printers, laser            printers, 3D printers, and plotters.        -   Audio and Video (AV) devices such as, but not limited to,            speakers, headphones, and lights, which include lamps,            strobes, DJ lighting, stage lighting, architectural            lighting, special effect lighting, and lasers.        -   Other devices such as Digital to Analog Converter (DAC).        -   Input/Output Devices may further comprise, but not be            limited to, touchscreens, networking device (e.g., devices            disclosed in network 962 sub-module), data storage device            (non-volatile storage 961), facsimile (FAX), and            graphics/sound cards.

V. Claims

While the specification includes examples, the disclosure's scope isindicated by the following claims. Furthermore, while the specificationhas been described in language specific to structural features and/ormethodological acts, the claims are not limited to the features or actsdescribed above. Rather, the specific features and acts described aboveare disclosed as example for embodiments of the disclosure.

Insofar as the description above and the accompanying drawing discloseany additional subject matter that is not within the scope of the claimsbelow, the disclosures are not dedicated to the public and the right tofile one or more applications to claims such additional disclosures isreserved.

The following is claimed:
 1. A method comprising: establishing at leastone target object to detect from a plurality of content streams, whereinestablishing the at least one target object to detect comprises:identifying at least one target object profile from a database of targetobject profiles; establishing at least one parameter for assessing theat least one target object, the at least one parameter being associatedwith a plurality of learned target object profiles trained by anArtificial Intelligence (AI) engine; calculating, with regard to the atleast one target object, at least one of the following: a time of daythe at least one target object is likely to be detected, and a locationthat the at least one target object is likely to be detected; providingat least one data point to an end-user, the at least one data pointcomprising at least one of the following: the time of day the at leastone target object is likely to be detected, and the location the atleast one target object is likely to be detected.
 2. The method of claim1, wherein providing the at least one data point comprises providing theat least one data point that enables the end user to track the at leastone detected target object.
 3. The method of claim 1, whereincalculating, with regard to the at least one target object, the mostlikely location of the at least one target object comprises: adetermining a tracking zone in which the at least one target object isprojected to be detected.
 4. The method of claim 1, further comprising:calculating a predicted geolocational direction of the at least onedetected target object.
 5. The method of claim 4, wherein providing theat least one data point comprising providing the predicted geolocationaldirection of the at least one detected target object.
 6. The method ofclaim 1, wherein establishing the at least one target object to detectfrom the plurality of content streams comprises monitoring a pluralityof zones associated with the plurality of content streams.
 7. The methodof claim 6, wherein monitoring the plurality of zones associated withthe plurality of content streams comprises monitoring at least onecontent capturing devices within each zone of the plurality of zones. 8.The method of claim 7, wherein each content stream of the plurality ofcontent streams is associated with a content capturing device within azone.
 9. The method of claim 1, wherein establishing the at least oneparameter comprises specifying at least one of the following: a speciesof the at least one target object, a sub-species of the at least onetarget object, a gender of the at least one target object, an age of theat least one target object, and a health of the at least one targetobject.
 10. One or more non-transitory computer-readable mediacomprising instructions which, when executed by one or more hardwareprocessors, causes performance of operations comprising: establishing atleast one target object to detect from a plurality of content streams,wherein establishing the at least one target object to detect comprises:identifying at least one target object profile from a database of targetobject profiles; establishing at least one parameter for assessing theat least one target object, the at least one parameter being associatedwith a plurality of learned target object profiles trained by anArtificial Intelligence (AI) engine; calculating, with regard to the atleast one target object, at least one of the following: a time of daythe at least one target object is likely to be detected, and a locationthat the at least one target object is likely to be detected; providingat least one data point to an end-user, the at least one data pointcomprising at least one of the following: the time of day the at leastone target object is likely to be detected, and the location the atleast one target object is likely to be detected.
 11. The one or morenon-transitory computer-readable media of claim 10, wherein providingthe at least one data point comprises providing the at least one datapoint that enables the end user to track the at least one detectedtarget object.
 12. The one or more non-transitory computer-readablemedia of claim 10, wherein calculating, with regard to the at least onetarget object, the most likely location of the at least one targetobject comprises: a determining a tracking zone in which the at leastone target object is projected to be detected.
 13. The one or morenon-transitory computer-readable media of claim 10, further comprising:calculating a predicted geolocational direction of the at least onedetected target object.
 14. The one or more non-transitorycomputer-readable media of claim 10, wherein providing the at least onedata point comprising providing the predicted geolocational direction ofthe at least one detected target object.
 15. The one or morenon-transitory computer-readable media of claim 10, wherein establishingthe at least one target object to detect from the plurality of contentstreams comprises monitoring a plurality of zones associated with theplurality of content streams.
 16. The one or more non-transitorycomputer-readable media of claim 15, wherein monitoring the plurality ofzones associated with the plurality of content streams comprisesmonitoring at least one content capturing devices within each zone ofthe plurality of zones.
 17. The one or more non-transitorycomputer-readable media of claim 16, wherein each content stream of theplurality of content streams is associated with a content capturingdevice within a zone.
 18. The one or more non-transitorycomputer-readable media of claim 10, wherein establishing the at leastone parameter comprises specifying at least one of the following: aspecies of the at least one target object, a sub-species of the at leastone target object, a gender of the at least one target object, an age ofthe at least one target object, and a health of the at least one targetobject.
 19. A system comprising: a memory storage; and a processing unitcoupled to the memory storage, the processing unit being configured to:establish at least one target object to detect from a plurality ofcontent streams, identify at least one target object profile from adatabase of target object profiles, establish at least one parameter forassessing the at least one target object, the at least one parameterbeing associated with a plurality of learned target object profilestrained by an Artificial Intelligence (AI) engine; calculate, withregard to the at least one target object, at least one of the following:a time of day the at least one target object is likely to be detected,and a location that the at least one target object is likely to bedetected; provide at least one data point to an end-user, wherein the atleast one data point comprises at least one of the following: the timeof day the at least one target object is likely to be detected, and thelocation the at least one target object is likely to be detected. 20.The system of claim 19, wherein the processing unit is furtherconfigured to determine a tracking zone in which the at least one targetobject is projected to be detected and provide the determined trackingzone to the end-user.